Azure Data Scientist Exam Dumps and DP-100 Associate Braindumps

In Contoso Machine Learning Studio the visual pipeline designer provides a drag and drop web based interface to build and run pipelines from built in or custom modules. When you submit a pipeline created with the visual designer it runs as a pipeline job and when you submit an Automated Machine Learning experiment it also runs as a job?

• ❏ A. False

• ❏ B. True

While using Vertex AI Workbench to build a custom model in a notebook you need to provision a compute VM from the terminal. What steps are essential to provision and tune the VM so it meets your experiment requirements?

• ❏ A. Use default VM settings and avoid any custom tuning

• ❏ B. Match the VM machine type memory CPU GPU count and disk size to the workload and account for cost trade offs

• ❏ C. Choose a high end VM without regard to cost or precise resource needs

• ❏ D. Use preemptible or spot VMs for batch runs and attach SSD persistent disks

A data science team at Skylark Analytics relies on Azure Machine Learning to host workspaces and managed developer machines. Compute Instances within a workspace provide a managed development environment alongside other workspace resources. Compute Instances include [A] and [B] installations which let practitioners write and run code that uses the Azure Machine Learning SDK to access workspace assets. Which words correctly complete the sentence?

• ❏ A. [A] Dataverse and [B] IoT Hub

• ❏ B. [A] Cloud Shell Editor and [B] Cloud Code

• ❏ C. [A] Jupyter Notebook and [B] JupyterLab

• ❏ D. [A] Anaconda Navigator and [B] RStudio

A lead data scientist named Maya Reyes at Meridian Research Center is deploying a batch scoring endpoint for an extract transform and load workflow and she has a deployment script ready she needs each execution to handle 90 records so which parameter should she set to guarantee that each run processes that number of records?

• ❏ A. instance_count

• ❏ B. output_action

• ❏ C. mini_batch_size

• ❏ D. scoring_script

A boutique firm named Meridian Analytics is adopting Microsoft Azure to host a low latency real time inference endpoint for a trained machine learning model that supports a mission critical application. The team needs to capture the input payloads that clients send to the service and the predictions the model returns while keeping operational and technical overhead to a minimum. Which action should the lead engineer take to provide an efficient monitoring solution for the deployed model?

• ❏ A. Configure an MLflow tracking server that targets the endpoint and inspect the logged runs

• ❏ B. Send metrics and logs to Azure Monitor and a Log Analytics workspace for the deployment

• ❏ C. Enable Azure Application Insights for the service endpoint and review telemetry in the Azure portal

• ❏ D. Examine the registered model explanations in Azure Machine Learning studio

A data science team at a fintech startup is configuring an Azure Machine Learning workspace and must specify the environment for training and deployment. Which items would be considered parts of an Azure Machine Learning environment definition? (Choose 2)

• ❏ A. Azure Kubernetes Service cluster

• ❏ B. The Docker base image

• ❏ C. Python interpreter version and library list

• ❏ D. A compute target such as a virtual machine size

Bramwell Clothiers is a heritage apparel chain with several stores across Greater Manchester and it recently bought a knitwear label in Barcelona. As part of integrating its systems with Microsoft Power Platform the lead data scientist Ava Stone is preparing to train a model and one of the input features contains sweater sizes labeled XXS XS S M and L. What preprocessing approach should Ava apply to encode the sweater size feature for machine learning?

• ❏ A. Target encoding

• ❏ B. Standardization

• ❏ C. One-hot encoding

• ❏ D. Ordinal encoding

• ❏ E. Normalization

After training a vehicle pricing model at Nova Mobility you must design a separate scoring workflow that applies the same data preprocessing to incoming records and then uses the stored model to assign price labels to those records. In machine learning terminology what does the act of using a trained model to produce label values for new examples mean?

• ❏ A. Measure correlation

• ❏ B. Generate predictions

• ❏ C. Compute a sum

• ❏ D. Make an estimate

• ❏ E. Calculate an average

A small consultancy named Brightlake Analytics is assembling a machine learning workflow in Azure Machine Learning Designer and needs to use a CSV file that is hosted on a public website and has not yet been created as a dataset. Which Designer module lets them ingest the CSV directly into the pipeline with minimal setup?

• ❏ A. Convert CSV to Dataset

• ❏ B. Create Dataset from Files

• ❏ C. Import Data

• ❏ D. Enter Data Manually

Beacon Restoration is a structural repair firm engaged by Metro City Emergency Services to restore metropolitan infrastructure after major incidents. Its CEO Evan Reed plans to add automated machine learning into company processes and he hires you as an Azure specialist. Your first assignment is to launch an AutoML training workflow. Which types of algorithms can AutoML pick for this training task? (Choose 2)

• ❏ A. Clustering

• ❏ B. Regression

• ❏ C. Dimensionality reduction

• ❏ D. Classification

• ❏ E. Time series forecasting

Maria Torres recently joined NovaSec Analytics as a data scientist. Her Azure Machine Learning pipeline ingests source files that exceed 3 GB each. To reduce I O and speed up distributed processing she must choose the most suitable file format for large scale machine learning workflows. Which file format should she select to maximize processing efficiency in Azure Machine Learning?

• ❏ A. TFRecords

• ❏ B. Apache Parquet

• ❏ C. XLSX

• ❏ D. CSV

When working inside an Azure Machine Learning workspace how do you produce a new version of an already registered dataset?

• ❏ A. Datasets will version automatically on a schedule that you configure

• ❏ B. Start a new training experiment that references the prior dataset and save the output as a separate dataset

• ❏ C. Load the updated data during a run and then register it as a dataset

• ❏ D. Register the updated files using the same dataset name as the previously registered dataset

All Azure Questions are from my DP-100 Udemy Course and certificationexams.pro

A regional orchard cooperative collects measurements such as rainfall totals soil nutrient indices and daily sunlight hours to estimate the yearly fruit harvest. Which type of machine learning model is most appropriate for forecasting a numeric harvest quantity?

• ❏ A. Classification model

• ❏ B. Reinforcement learning model

• ❏ C. Unsupervised learning model

• ❏ D. Regression model

You have developed a regression model for a consumer insights team at Pine Street Analytics and you want to assess how one particular feature affected a single model prediction. Which tool within “Explainer” would you use?

• ❏ A. Global feature importance

• ❏ B. Partial dependence plot

• ❏ C. Local feature importance

• ❏ D. Label influence analysis

Astra Collective is a well funded research consortium that founded the orbital hub Starhaven and the lead engineer Marik Volan is introducing Microsoft Azure to the team and they plan to use HyperDrive for hyperparameter tuning and the engineer wrote the following code to define the search space and run configuration import azureml.train.hyperdrive.parameter_expressions as pe from azureml.train.hyperdrive import GridParameterSampling, HyperDriveConfig param_sampling = GridParameterSampling({ “max_depth”: pe.choice(5,7,9,11), “learning_rate”: pe.choice(0.06,0.12,0.18) }) hyperdrive_run_config = HyperDriveConfig(estimator=estimator, hyperparameter_sampling=param_sampling, policy=None, primary_metric_name=”auc”, primary_metric_goal=PrimaryMetricGoal.MAXIMIZE, max_total_runs=60, max_concurrent_runs=5) Which of the following statements is true?

• ❏ A. The experiment will produce trials for every numeric value in the 0.006 to 0.18 range for learning_rate

• ❏ B. None of the listed statements is correct

• ❏ C. The run will perform 60 trials for this hyperparameter search

• ❏ D. They can assign a security policy to the policy argument of HyperDriveConfig

A data science group at Crestline Insights is comparing model families for a prediction task because their datasets are relatively small and interpretability is important. Which model type best matches the following description: “These constructs are not just one decision tree, but a large number of trees, allowing better predictions on more complex data. Widely used in machine learning and science due to their strong prediction abilities.”?

• ❏ A. Vertex AI

• ❏ B. Least squares regression

• ❏ C. Ensemble models

• ❏ D. Linear regression

DataWave Analytics has published a live prediction model to an HTTP endpoint and you want to test it from a client. Which statement about how many records you may send per request and which data formats the endpoint accepts is correct?

• ❏ A. The endpoint accepts a single record per request and the payload may be JSON or CSV

• ❏ B. The endpoint only accepts multiple records in a single call and the body must be JSON

• ❏ C. The endpoint accepts a batch of records in a single request and the payload may be JSON or CSV

• ❏ D. The endpoint supports only one record per request and the body must be JSON

Context. Meridian Analytics is a data science firm led by CEO Clara Meridian with a valuation above thirty five million dollars. The team is preparing to use Microsoft Azure Machine Learning and they have published a model as a live inferencing endpoint that is hosted on Azure Kubernetes Service. What actions must the engineering group perform to collect and examine telemetry for the AKS hosted inferencing endpoint?

• ❏ A. Enable Azure Monitor for containers

• ❏ B. Enable Application Insights and associate it with the workspace and the deployed service

• ❏ C. Redeploy the model to Azure Container Instances

• ❏ D. Move the AKS cluster into the same region as the Azure Machine Learning workspace

Novagen Materials is a multinational materials manufacturer based in Seattle that produces polymers and specialty compounds for consumer and industrial markets. The chief technology officer Mia Torres has engaged you as a senior consultant for the technology team. One of the engineers Sam Fisher is applying K-Means clustering as part of a machine learning pipeline. Which category of machine learning is Sam using?

• ❏ A. Reinforcement learning

• ❏ B. K Nearest Neighbors

• ❏ C. Unsupervised learning

• ❏ D. Supervised learning

Dr. Maya Patel a machine learning researcher at Meridian General Clinic is running experiments where she varies hyperparameters and network structures and she needs a reliable way to persist and manage different iterations of her model artifacts and associated metadata inside her Azure ML workspace. What method should she use to store and catalog distinct versions of her machine learning model?

• ❏ A. Deploy the trained model to an endpoint

• ❏ B. Use child runs to organize experiment trials

• ❏ C. Enable Application Insights telemetry

• ❏ D. Register the model in the workspace model registry

All Azure Questions are from my DP-100 Udemy Course and certificationexams.pro

A research team at a startup is training a deep convolutional neural network for object recognition and they notice the model is overfitting on the validation set. To reduce overfitting and help the model generalize better what approach is most effective?

• ❏ A. Add dropout layers and enable batch normalization

• ❏ B. Perform transfer learning with a pretrained backbone and freeze most layers

• ❏ C. Apply L1 and L2 penalty terms during training and augment the training images

• ❏ D. Increase the network capacity by adding a 1024 neuron dense layer and reduce the number of training examples

The Morning Ledger is a regional newspaper led by Edmund Grant that expanded rapidly from a small team into a widely read outlet and the company hired you as an IT consultant to improve systems and workflows. One active assignment is to build an experiment in Azure Machine Learning Studio and the dataset for the experiment has an imbalanced target where one class is much rarer than the others. The lead developer Lena Ross picked Stratified split as the sampling mode. Does the choice made by Lena Ross meet the project objective?

• ❏ A. Random undersampling

• ❏ B. Stratified split sampling

• ❏ C. Use SMOTE sampling mode

• ❏ D. Random split sampling

Scenario: The Orion Consortium is a research foundation that handles large scale analytics and it has recently added Microsoft Azure to its infrastructure. The engineering group built a batch scoring pipeline with the Azure ML SDK and they start it with this code python from azureml.pipeline.core import Pipeline from azureml.core import Experiment pipeline = Pipeline(workspace=ws, steps=[batch_step]) pipeline_run = Experiment(ws, ‘bulk_job_v3’).submit(pipeline) The team needs to observe the pipeline progress as it runs. Which methods can they use to monitor the pipeline execution? (Choose 2)

• ❏ A. Use the RunDetails widget in a notebook by running RunDetails(pipeline_run).show()

• ❏ B. Check metrics and logs from the Kubernetes cluster in Azure Monitor

• ❏ C. Call pipeline_run.wait_for_completion(show_output=True) and watch the console output

• ❏ D. Open the Inference Clusters tab in Machine Learning Designer

Rafferty’s Burgers is a regional quick service restaurant chain that is modernizing its analytics platform with Microsoft Azure. You are leading a technical session on training supervised models. The data science team has created a scikit learn LinearRegression instance and they are ready to run training. Which method should the team call to train the LinearRegression estimator?

• ❏ A. Call the predict method with the training feature matrix and the training labels

• ❏ B. Invoke the corr method on the model object and supply the feature and target arrays

• ❏ C. Call the score method on the estimator and pass the training feature matrix and the target array

• ❏ D. Call the fit method on the LinearRegression instance with the feature matrix and the target vector

Scenario: Meridian Robotics was established in 1952 by Elena Park and grew into a major technology firm. After Elena retired in 2005, Rupert Hale served briefly as acting CEO before her daughter Maya Park assumed leadership. Maya is coordinating with other engineers using a shared Git repository for a model development project. She plans to clone the Git repository onto the local file system of an Azure Machine Learning compute instance so she can work on the code. What first action should Maya perform before cloning the repository?

• ❏ A. Generate a new SSH key pair

• ❏ B. Launch an Azure Cloud Shell session

• ❏ C. Open a terminal on the Azure Machine Learning compute instance

• ❏ D. Add the public SSH key to the remote Git hosting account

While training a binary classification model in Nexa Machine Learning Studio you plan to run a parameter sweep to tune hyperparameters and your objectives are to sample many hyperparameter combinations while minimizing compute usage which sweep approach should you select?

• ❏ A. Measured grid sweep mode

• ❏ B. Vertex AI Hyperparameter Tuning

• ❏ C. Randomized grid sweep mode

• ❏ D. Exhaustive grid sweep mode

Maya Chen is a data engineer at Orbit Labs and she has been using pandas on her laptop to prepare tables for model training, but a recent surge to about 2.4 TB of tabular data means she needs to process the data faster while keeping operational overhead low using Azure Machine Learning. Which Azure Machine Learning capability should she choose to wrangle large tabular datasets efficiently while minimizing resource management?

• ❏ A. Azure Databricks notebook

• ❏ B. Standalone Spark job

• ❏ C. Spark component in a pipeline job

• ❏ D. Azure Machine Learning compute instance

A data science team at DataSphere Labs is converting interactive machine learning notebooks into automated scripts for scheduled pipelines and production runs. Which practices should they adopt to make the scripts clean maintainable and automation friendly? (Choose 2)

• ❏ A. Refactor code into smaller reusable functions to improve readability maintenance and testability

• ❏ B. Convert explanatory notebook markdown cells into inline comments inside the script

• ❏ C. Add structured logging and comprehensive error handling to support monitoring and operations

• ❏ D. Strip out exploratory fragments and print statements to streamline the script for automation

In the Stratus ML Studio environment the term data wrangling is frequently used as an essential step. Which of the following best describes data wrangling?

• ❏ A. Splitting datasets into training testing and validation subsets

• ❏ B. Transforming and cleaning raw datasets into structured formats suitable for machine learning models

• ❏ C. Managing storage governance and sharing of data among analysts and teams

• ❏ D. Being identical to simple preprocessing tasks such as scaling and basic imputations

Scenario: Nova Robotics is a US based robotics manufacturer led by Eve Carter and located on Alameda Point California. The engineering team is running an experiment with Azure Machine Learning to train a classifier and plans to use HyperDrive to tune hyperparameters to maximize the AUC metric. A developer set up HyperDrive with estimator titan_estimator and hyperparameter_sampling eve_params and policy policy and primary_metric_name “AUC” and primary_metric_goal PrimaryMetricGoal.MAXIMIZE and max_total_runs 9 and max_concurrent_runs 3. The training script computes a random forest model and validates it using test data where the true labels are in y_test and the predicted probabilities are in y_scores. The current script imports json and os and from sklearn.metrics imports roc_auc_score and imports logging and then computes auc = roc_auc_score(y_test, y_scores) and writes that value to outputs/AUC.txt. To enable HyperDrive to optimize on the AUC metric you must add logging so the metric is visible to HyperDrive. What logging changes should be made to the script to allow HyperDrive to detect the AUC metric?

• ❏ A. Use a print statement to write “AUC: ” plus the calculated value

• ❏ B. Only save the numeric AUC to a file under outputs

• ❏ C. Call Run.get_context and use run.log with the metric name and value

• ❏ D. Use logging.info to emit a line like “AUC: ” plus the AUC value

All Azure Questions are from my DP-100 Udemy Course and certificationexams.pro

Fill in the blank in the following statement in the context of Google Cloud. [__] is a type of machine learning used to assign items to categories or classes. For instance a community clinic could use patient attributes such as age body mass index and blood pressure to estimate whether a person has diabetes. Which word or words complete the sentence?

• ❏ A. Multinomial

• ❏ B. Binomial

• ❏ C. Logistic regression

• ❏ D. Redundancy

• ❏ E. Classification

• ❏ F. Ordinal

You are designing a hyperparameter search job on Contoso Machine Learning Platform and you must choose a sampling method while defining the search domain. What strategy will allow you to explore the hyperparameter landscape effectively while keeping compute costs reasonable?

• ❏ A. Use Bayesian optimization to model the performance surface and iteratively propose promising configurations

• ❏ B. Narrow the parameter ranges and tune only a couple of variables to speed up experiments

• ❏ C. Apply random sampling across the parameter domain to achieve broad coverage with moderate compute

• ❏ D. Run an exhaustive grid search over all parameter combinations regardless of compute constraints

Scenario Dr Elena Frost is leading a migration at Aurora Analytics as the team moves legacy on premise systems to cloud environments and her colleague Maya has already cleaned and prepared the training dataset and now wants to run Azure automated machine learning while keeping the dataset unchanged during experimentation Which featurization setting should Maya choose so that AutoML does not modify the data?

• ❏ A. custom

• ❏ B. off

• ❏ C. auto

• ❏ D. manual

Dr Elena Rivers is leading a migration of on premise systems for the Aurora Research Lab because the servers are nearing retirement. Marco is one of the engineers packaging a machine learning task with MLflow as part of the effort. Which two essential assets must an MLflow project include to ensure it runs correctly and that results are reproducible?

• ❏ A. A registered model artifact and its evaluation metrics

• ❏ B. A Docker image and a model registry entry

• ❏ C. A Python entry script and an environment specification

• ❏ D. An MLproject descriptor file and a dataset snapshot

Scenario: The Pacific Analytics Collective founded by Daniel Reyes is using Microsoft Azure to streamline operations and they have engaged you to build a classification model from CRM records. You implemented a pipeline that first cleans incoming records and then trains the model and the data cleaning task must run each day at 3 00 AM. Which schedule type in Azure Machine Learning should be used to automate the daily execution of the cleaning step?

• ❏ A. frequency=”hour”, interval=3

• ❏ B. 3 0 * * *

• ❏ C. 0 3 * * *

• ❏ D. frequency=”day”, interval=2

Arcadia Robotics is a U.S. industrial manufacturer led by Maya Rivera and based at Marina Point in San Francisco California. The firm is expanding quickly and the IT director has asked you to improve experiment tracking. The data science group will use the Azure Machine Learning Python SDK to write an experiment. They must capture metrics for each experiment run and be able to retrieve those metrics efficiently for later analysis. Which approach should the team use to log and retrieve metrics with the Azure Machine Learning Python SDK?

• ❏ A. Rely on print statements in the script to emit metrics to stdout

• ❏ B. Call the Run.log and Run.log_list methods on the Run object to record named metrics for each run

• ❏ C. Send metrics to Application Insights and query them later

• ❏ D. Write metric files into the run outputs folder for later download

Which compute service should BrightLake Analytics choose to host a low latency online model endpoint for real time inference?

• ❏ A. Cloud Run

• ❏ B. Google Kubernetes Engine

• ❏ C. Google Compute Engine

• ❏ D. On premise servers

The City Tribune is a regional publication overseen by editor Alex Mercer and the newsroom has hired you to streamline their data pipelines. One assignment involves using Azure Machine Learning Studio for feature engineering on a dataset. The team must normalize a numeric field to produce an output column of bins that will be used as a predictor for a target variable. The editor instructed the group to apply Quantiles normalization along with QuantileIndex normalization. Does this instruction support the objective of creating binned values for the predictive target?

• ❏ A. Use the Bin Data module with manually specified bin edges

• ❏ B. Normalize values using ZScore and then apply equal width binning

• ❏ C. Apply Quantiles normalization by itself without creating a QuantileIndex mapping

• ❏ D. Use Quantiles normalization together with QuantileIndex normalization

A machine learning team at Helix Analytics has an image dataset that is publicly available via a URL and they plan to register it in their Acme Machine Learning project. When creating a data asset to point to this collection which asset type should they select to organize multiple image files efficiently?

• ❏ A. Tabular asset

• ❏ B. Stream recording asset

• ❏ C. Directory asset

• ❏ D. Single file asset

Scenario: Nova Instruments is a United States engineering manufacturer led by Maya Chen and located on Harbor Point in the San Francisco Bay. The company is growing quickly and the IT department must establish a development platform for both data engineering and data science. The platform needs to support Python and Scala, enable the design and automation of data pipelines that handle storage movement and processing, provide a single orchestration solution for engineering and science workflows, allow workload isolation and interactive sessions, and scale across a cluster of machines. Which approach best satisfies these requirements?

• ❏ A. Build the solution using Hive on an HDInsight cluster and coordinate pipelines with Azure Data Factory

• ❏ B. Run workloads on Azure Databricks and use Azure Container Instances for orchestration

• ❏ C. Create an Apache Spark environment on HDInsight and use Azure Kubernetes Service to orchestrate workflows

• ❏ D. Deploy the environment on Azure Databricks and orchestrate pipelines with Azure Data Factory

Is it possible to create virtual machine instances by using the cloud provider’s Python client library?

• ❏ A. gcloud CLI

• ❏ B. You can create VM instances with the Python client library

• ❏ C. Compute Engine REST API

• ❏ D. No the Python client is only for data preparation model training and deployment

Nova Energy Research is a development lab in Boulder Colorado. Its lead scientists Mara and Diego uncovered an ancient codex and used its insights to build Quantum Cells. Mara trained a linear regression model named NER_Model and now needs to assess its performance. Which code should be executed to correctly validate the NER_Model?

• ❏ A. predictions = NER_Model.convert(trainingData)

• ❏ B. predictions = NER_Model.fit(trainingData)

• ❏ C. predictions = NER_Model.transform(validationData)

• ❏ D. predictions = NER_Model.predict(validationData)

Contoso Automated Machine Learning helps practitioners who lack deep data science experience assemble end to end machine learning pipelines. What is another significant benefit of using this AutoML solution?

• ❏ A. Catalog of ready made pretrained models built from public datasets

• ❏ B. Validate newly created custom pipeline components

• ❏ C. Vertex AI

• ❏ D. Automatically search for top performing algorithms and hyperparameter settings for a given problem

All Azure Questions are from my DP-100 Udemy Course and certificationexams.pro

A regional ride company called Riverview Cabs is training a regression model to predict trip fares and it needs to choose evaluation metrics that reflect regression performance accurately. Which two metrics are most appropriate for assessing this type of regression model? (Choose 2)

• ❏ A. A large Root Mean Squared Error value

• ❏ B. An R squared value near one

• ❏ C. A low Root Mean Squared Error value

• ❏ D. A high F1 score

• ❏ E. An R squared value close to zero

• ❏ F. A low F1 score

A mid sized analytics firm named Meridian Analytics hired your team to help with an Azure data science deployment and they provided a NumPy array with six elements defined as data = array([5, 15, 25, 35, 45, 55]) and they want to use scikit learn k fold cross validation to produce three splits where each training set contains four elements and each test set contains two elements as in train [5 35 45 55] test [15 25] train [15 25 35 55] test [5 45] train [5 15 25 45] test [35 55] An intern left placeholders [A], [B], and [C] in this incomplete code snippet and you must decide which identifiers should replace them to make the script run correctly?

• ❏ A. Replace [A] with StratifiedKFold replace [B] with 3 and replace [C] with data

• ❏ B. Replace [A] with ShuffleSplit replace [B] with 3 and replace [C] with data

• ❏ C. Replace [A] with KFold replace [B] with 3 and replace [C] with data

• ❏ D. Replace [A] with KMeans replace [B] with 6 and replace [C] with train

• ❏ E. Replace [A] with GroupKFold replace [B] with 3 and replace [C] with data

• ❏ F. Replace [A] with cross_validate replace [B] with 3 and replace [C] with array

Dr Maya Li is advising Solstice Data Labs on a retail analytics project and she is building a deep neural network that will classify products into three categories using 12 numeric features. Which of the following statements about the network architecture is true?

• ❏ A. Vertex AI

• ❏ B. The input layer should have three neurons

• ❏ C. The output layer should contain three neurons

• ❏ D. The input layer should contain six neurons

RapidShip Logistics is led by Marco Rossi from its European office in Milan Italy and recently hired Elena Vega as a data scientist. Elena plans to run a training script from her preferred development environment and monitor the experiment using Azure Machine Learning. Which tool should she use to execute the training script from her Python environment?

• ❏ A. Azure HDInsight

• ❏ B. Azure CLI

• ❏ C. Azure Machine Learning studio

• ❏ D. Azure Machine Learning Python SDK

A data engineering team at Nova Analytics plans to provision a Data Science Virtual Machine to run open source deep learning libraries such as Caffe2 and PyTorch and they want an image that is most compatible with those tools. Which DSVM edition should they choose?

• ❏ A. Data Science Virtual Machine for Windows 2018

• ❏ B. Geo AI Data Science Virtual Machine with ArcGIS

• ❏ C. Data Science Virtual Machine for Linux (Debian)

• ❏ D. Data Science Virtual Machine for Windows 2014

• ❏ E. Data Science Virtual Machine for Linux (Ubuntu)

Scenario: Nova Nebula Analytics has engaged your group to design a new deep learning pipeline. The task is to assemble a pipeline that prepares data and trains the model with the Azure Machine Learning SDK v2. The sample Python script configures Azure authentication and creates an MLClient but the import that provides the pipeline decorator is left as a placeholder. Which import should replace the placeholder so the pipeline decorator is correctly available?

• ❏ A. from azure.ai.ml import pipeline

• ❏ B. azure.pipeline

• ❏ C. from azure.ai.ml.dsl import pipeline

• ❏ D. azure.ai.ml.dsl.pipeline

• ❏ E. azureml.pipeline.core.Pipeline

Pemberton Analytics is a data research firm led by Aisha Pemberton and she has asked her team to create custom roles for their machine learning workspace. What does defining custom roles enable you to do?

• ❏ A. Cloud IAM predefined roles manage access at the project level

• ❏ B. Custom roles change the synthesized voice used by services

• ❏ C. Custom roles let you define allowed actions and explicit deny rules so you can grant and restrict access to specific workspace resources

• ❏ D. Custom roles restrict a user to view only operations within a workspace

While tuning hyperparameters for a forecasting model at Arcadia Analytics what is true about Bayesian sampling methods?

• ❏ A. Bayesian sampling can be paired with an early stopping policy

• ❏ B. Bayesian sampling is restricted to only uniform choice and quniform parameter types

• ❏ C. Bayesian sampling always finds the absolute best configuration and is the slowest approach

• ❏ D. Vertex AI Vizier

As a consultant at Sentinel Data Labs you are advising Riley the head of IT on their Azure Machine Learning deployment and the team has submitted and finished a training job in Azure Machine Learning and they now need to retrieve the job metrics inside a Jupyter Notebook using the MLflowClient class from the Azure ML Python SDK v2 which MLflowClient method should they call to obtain the run metrics?

• ❏ A. log_artifact()

• ❏ B. get_metric_history()

• ❏ C. get_run()

• ❏ D. log_metric()

Scenario: Meridian Retail Insights is a division of Hargrave Holdings based in Albany New York and led by Elena Cortez. Elena plans to publish an online endpoint for price forecasting and she needs to set the endpoint name instance type runtime environment and code configuration using the proper class. Which class should she use for this deployment?

• ❏ A. NetworkSettings

• ❏ B. Model

• ❏ C. ManagedOnlineDeployment

• ❏ D. OnlineEndpoint

A data team at an urban scooter rental startup is comparing regression loss metrics. The coefficient of determination also called R squared yields a value between 0 and 1 that reflects how much of the variance the model explains. Generally the closer this value is to which number does it indicate stronger predictive performance?

• ❏ A. Explained variance score

• ❏ B. Zero

• ❏ C. One

• ❏ D. The mean value of the label

• ❏ E. The correlation coefficient

How do a Compute resource and an Environment differ in their roles and responsibilities within a machine learning workflow?

• ❏ A. An Environment is the hardware specification that hosts Computes while Computes are the software images that execute the workload

• ❏ B. Compute refers to the underlying virtual or physical resources that run workloads and Environment encapsulates the software dependencies and runtime needed to execute the code

• ❏ C. A Compute cluster is capable of spanning multiple nodes whereas an Environment is restricted to a single execution instance

• ❏ D. An Environment is equivalent to an image registry and Compute simply pulls images from that registry to run tasks

True is correct because both a pipeline created with the visual designer and an Automated Machine Learning experiment run as jobs when you submit them.

When you submit a visual designer pipeline it runs as a pipeline job that is tracked by the Azure Machine Learning service and can target compute, capture outputs, and be monitored like other jobs. Automated Machine Learning experiments also run as jobs and they create tracked runs that record metrics, models, and artifacts for review and deployment.

False is incorrect because the statement is accurate and both submission types are executed and tracked as jobs rather than as simple one off tasks without job metadata.

When a question mentions you submit or run work in Azure Machine Learning think about whether the platform treats them as jobs. That mapping usually tells you if designer pipelines and Automated ML produce tracked runs.

Match the VM machine type memory CPU GPU count and disk size to the workload and account for cost trade offs is the correct option.

This choice is correct because provisioning a VM that aligns with your experiment workload ensures you have enough CPU and memory for preprocessing and training and the right GPU type and count for model acceleration. It also means sizing disk capacity and I O performance to match dataset size and training checkpoint frequency while balancing cost so you do not overpay for unused capacity. Start with a reasonable estimate based on profiling, monitor resource utilization during runs, and iterate so that the VM meets performance targets without unnecessary expense.

Use default VM settings and avoid any custom tuning is wrong because default settings are generic and often underprovisioned or improperly balanced for tasks that need GPUs or heavy I O. Relying on defaults can lead to slow experiments or unexpected failures when the workload demands more specialized resources.

Choose a high end VM without regard to cost or precise resource needs is wrong because blindly selecting the largest instance wastes budget and may still not match the right resource profile for your workload. It is better to size to needs and scale up only when monitoring shows a bottleneck.

Use preemptible or spot VMs for batch runs and attach SSD persistent disks is wrong as a general essential step because while preemptible or spot VMs can reduce cost for tolerant batch jobs, they are interruptible and require checkpointing and retry logic. Attaching SSD persistent disks can help I O but does not eliminate the risks of preemption and is not universally appropriate for all experiments.

Profile a small run to observe CPU memory GPU and disk usage and then adjust the VM size so you balance performance and cost.

[A] Jupyter Notebook and [B] JupyterLab are correct.

Compute Instances in Azure Machine Learning provide managed development VMs that include installations of Jupyter Notebook and JupyterLab so practitioners can write and run code and use the Azure Machine Learning SDK to access workspace assets such as datasets, models, and experiments.

[A] Dataverse and [B] IoT Hub is incorrect because Dataverse is a data platform and IoT Hub is a service for device messaging and neither one is a local interactive development environment or notebook installation on a compute instance.

[A] Cloud Shell Editor and [B] Cloud Code is incorrect because these are tooling options for command line and IDE integration and they are not the preinstalled notebook interfaces provided by Azure Machine Learning compute instances.

[A] Anaconda Navigator and [B] RStudio is incorrect because Anaconda Navigator is a desktop GUI for package and environment management and RStudio is an IDE for R which is not the default notebook interface shipped on Azure ML compute instances, although you can configure custom images if you need other tools.

When a question mentions compute instances look for answers that reference interactive notebook environments and rule out items that are platform services or tooling plugins rather than built in notebook interfaces.

The correct option is mini_batch_size.

mini_batch_size specifies how many input records are grouped into each mini batch for a batch scoring run, and setting it to 90 guarantees that each execution receives 90 records to process. This parameter is used by batch inference and parallel run configurations to control the unit of work handed to the scoring code so it is the right place to set a fixed per-run record count.

instance_count controls how many compute instances are allocated to the job and it does not guarantee how many records each execution will handle. Adjusting instance_count changes parallelism but not the per-execution batch size.

output_action determines how results are returned or stored and it does not set the number of records processed per run. That option affects output handling rather than the size of each mini batch.

scoring_script is the code that processes incoming records and it defines the processing logic but it is not the orchestration parameter that fixes how many records are passed in each run. You still need to set mini_batch_size to control the number of records delivered to the script.

When a question asks which parameter sets the number of records per execution look for names that include mini or batch as they usually control per-run record counts.

The correct option is Enable Azure Application Insights for the service endpoint and review telemetry in the Azure portal.

Azure Application Insights is purpose built to collect request and response telemetry for web and API endpoints and it integrates with Azure services so you can view live metrics and traces in the portal with minimal operational overhead. You can enable automatic request collection and add lightweight custom telemetry to capture input payloads and model outputs while using sampling and other controls to keep performance impact low. The portal gives you built in tools to query and visualize telemetry and you can forward data to other stores if you need longer retention or advanced analytics.

Configure an MLflow tracking server that targets the endpoint and inspect the logged runs is not the best choice because MLflow is designed for experiment tracking and model lifecycle metadata rather than lightweight production request and response telemetry. Running a dedicated MLflow tracking server also adds infrastructure and operational work that contradicts the requirement to keep overhead to a minimum.

Send metrics and logs to Azure Monitor and a Log Analytics workspace for the deployment is technically possible but it usually requires more custom plumbing to capture full request and response payloads and to correlate traces. This approach can increase operational complexity compared to enabling Application Insights which is already instrumented to collect endpoint telemetry and integrate with the portal.

Examine the registered model explanations in Azure Machine Learning studio is not appropriate because registered explanations provide interpretability artifacts and not a continuous capture of runtime input payloads and model predictions. Those artifacts are useful for understanding model behavior but they do not replace real time telemetry for a mission critical inference endpoint.

When a question asks for low operational overhead and real time request and response capture prefer services that provide built in telemetry for endpoints such as Application Insights.

The correct options are The Docker base image and Python interpreter version and library list.

The Docker base image is part of an Azure Machine Learning environment because it defines the container image that provides the underlying operating system layer and system packages used during training and inference.

Python interpreter version and library list are part of the environment because environments capture language runtimes and dependency manifests so experiments and deployed models run with consistent packages and versions.

Azure Kubernetes Service cluster is incorrect because it is a compute or deployment target where workloads run rather than a description of software dependencies and runtime.

A compute target such as a virtual machine size is incorrect because it specifies the hardware or resource allocation for training or inference and not the environment that defines packages or base images.

Separate the concept of environment from compute when you read the question. Environments capture software and dependencies and compute targets capture where the workload runs.

The correct option is One-hot encoding.

One-hot encoding creates a separate binary feature for each size so the model does not assume any numeric ordering or spacing between categories. This works well for a small set of distinct labels like XXS, XS, S, M, and L because it preserves category identity without introducing artificial numeric relationships.

Target encoding is not appropriate because it replaces categories with statistics derived from the target and can leak information and cause overfitting, and it is mainly used for very high cardinality features.

Standardization is not suitable because it rescales continuous numeric features to zero mean and unit variance and does not convert categorical labels into usable numeric features.

Ordinal encoding is not the best choice here because it assigns integer values that impose an order and assume equal spacing between sizes, which can mislead models unless you have a validated numeric scale for the differences between sizes.

Normalization is also inappropriate because it rescales numeric vectors to a fixed norm and does not provide a method to encode categorical labels into distinct numeric features.

When a categorical feature has a few distinct labels and no reliable numeric spacing, prefer one-hot encoding. Reserve ordinal encoding for cases where an ordered feature has meaningful and comparable numeric gaps.

The correct option is Generate predictions.

Generate predictions means applying a trained model and the same preprocessing steps to new input records so the model can produce label values or scores for those records. This step is commonly called scoring or inference and it is exactly what a production scoring workflow performs.

Measure correlation refers to quantifying relationships between variables and not to using a trained model to assign labels, so it is incorrect.

Compute a sum describes a basic arithmetic aggregation and not the process of running a model to produce predictions, so it is incorrect.

Make an estimate is an informal phrase that could loosely describe prediction in everyday language but it is not the precise machine learning term the question asks for, so it is incorrect.

Calculate an average is a statistical aggregation operation and does not describe model inference or scoring, so it is incorrect.

When a question asks about applying a trained model to new data think of the terms prediction and inference and choose the option that matches that exact terminology.

All Azure Questions are from my DP-100 Udemy Course and certificationexams.pro

The correct option is Import Data.

The Import Data module in Azure Machine Learning Designer is designed to pull data directly from external sources and it can read a CSV hosted on a public website with minimal setup. You can drop the Import Data module into your pipeline, configure the HTTP or HTTPS URL and format settings, and the module outputs data that downstream components can consume.

Convert CSV to Dataset is not the standard Designer module name for ingesting a remote CSV and it does not describe the built in module that reads from a web URL, so it is not the correct choice.

Create Dataset from Files refers to creating and registering a dataset in the workspace from file storage or uploads and it usually requires selecting storage or uploading files outside the pipeline, so it is not the minimal inline import from a public URL.

Enter Data Manually is for small manual tables entered directly in the interface and it is not appropriate for fetching a CSV file from a public website.

When a question mentions ingesting a CSV from a public URL in Designer look for the module that accepts a web address and outputs a dataset. Import Data is the module that does this with the least configuration.

The correct options are Regression and Classification.

Regression is correct because AutoML automates the selection and hyperparameter tuning of models that predict continuous numeric targets and it evaluates models using regression metrics while trying algorithms such as linear models and tree ensembles.

Classification is correct because AutoML also handles predicting categorical labels and it evaluates and optimizes classifiers using metrics like accuracy and AUC while testing a range of classification algorithms.

Clustering is not correct because clustering is an unsupervised grouping task that does not use labeled targets and it is not the focus of a supervised AutoML training workflow.

Dimensionality reduction is not correct because dimensionality reduction is a preprocessing or feature engineering technique rather than a target predictive task that AutoML selects as the model objective.

Time series forecasting is not correct for this training task because forecasting is a specialized scenario that requires different setup and was not included among the target tasks for this AutoML workflow.

When in doubt identify whether the problem is supervised or unsupervised and map numeric targets to regression and categorical labels to classification.

Apache Parquet is the correct choice for maximizing processing efficiency in Azure Machine Learning.

Apache Parquet is a columnar file format and it reduces I O by reading only the columns that are needed instead of whole rows. It supports efficient compression and encoding schemes which lowers storage size and speeds up data transfer which is important for files larger than 3 GB. The format is also splittable which enables parallel reads by distributed compute engines and that improves throughput for large scale machine learning workflows.

TFRecords is optimized for TensorFlow sequential record consumption and it is not a columnar, schema rich format. It can be efficient for TensorFlow training pipelines but it does not provide the same column pruning and wide ecosystem support for analytics engines as Parquet.

XLSX is a spreadsheet format that is not designed for large scale distributed processing. It has significant parsing overhead and it is not splittable which makes it unsuitable for multi node reads of multi gigabyte files.

CSV is a simple row based text format and it lacks an explicit schema and efficient columnar storage. CSV files often require more I O to scan and more CPU to parse which slows distributed processing compared with a compressed, columnar format like Parquet.

For large datasets prefer columnar and splittable formats with built in compression when you need to minimize I O and maximize parallel processing on the exam and in real projects.

The correct option is Register the updated files using the same dataset name as the previously registered dataset.

Registering updated files under the same dataset name causes Azure Machine Learning to create a new version of the dataset while preserving prior versions. The registry records the data paths and metadata and increments the dataset version so you can reference the specific version used in experiments and reproduce results.

Datasets will version automatically on a schedule that you configure is incorrect because Azure Machine Learning does not provide built in scheduled versioning. You can automate registration with scripts or pipelines to mimic a schedule but the service only creates a new version at registration time.

Start a new training experiment that references the prior dataset and save the output as a separate dataset is incorrect because running training that uses an existing dataset does not produce a new version of that dataset. The outputs of a run are separate artifacts and do not increment the original dataset version unless you explicitly register the updated files with the same dataset name.

Load the updated data during a run and then register it as a dataset is misleading and therefore incorrect in this context because simply loading data in a run does not by itself version the prior dataset. You must explicitly register the updated files using the same dataset name if you want a new version to be recorded in the dataset registry.

When you want a new dataset version remember to register the updated files with the same dataset name. Practice distinguishing between dataset registration and the outputs produced by training runs.

The correct answer is Regression model. A Regression model predicts continuous numeric outcomes and it is the best fit for forecasting a yearly harvest quantity from inputs such as rainfall totals soil nutrient indices and daily sunlight hours.

Regression model is a supervised learning approach that learns the relationship between input features and a continuous target. Common algorithms include linear regression decision tree regression and gradient boosted trees which can model linear and non linear effects in the data to produce a numeric prediction for harvest size.

Classification model is designed to assign discrete labels or categories rather than predict a continuous numeric value so it is not appropriate for estimating a harvest quantity.

Reinforcement learning model is meant for agents that learn by taking actions and receiving rewards in an environment. It is not the standard approach for supervised forecasting from historical measurement data.

Unsupervised learning model is used to discover patterns or groupings in unlabeled data such as clusters or principal components. It does not directly produce a labeled numeric target like yearly harvest unless it is combined with a separate supervised method.

Look for words that indicate a continuous numeric target such as quantity or amount. Those clues usually point to a regression approach rather than classification or unsupervised methods.

The correct option is Local feature importance.

Local feature importance provides an explanation for a single prediction and shows how each feature contributed to that specific output. This type of explanation gives attribution scores for the instance you care about so you can see whether a particular feature increased or decreased the predicted value.

Global feature importance is incorrect because it summarizes feature effects across the whole dataset and does not tell you how a feature influenced one particular prediction.

Partial dependence plot is incorrect because it shows the average relationship between a feature and the prediction across many samples and it is not a per instance attribution method.

Label influence analysis is incorrect because it focuses on how training labels or training examples affect model behavior and not on attributing an individual prediction to its features.

When the question asks about how a feature affected a single prediction look for the word local or the phrase per instance and avoid answers that describe global or average effects.

The correct answer is None of the listed statements is correct.

This is correct because the code uses GridParameterSampling with discrete choices for the parameters and not a continuous range. The parameter expressions call pe.choice(5,7,9,11) for max_depth and pe.choice(0.06,0.12,0.18) for learning_rate so the grid contains 4 times 3 which equals 12 distinct combinations. The None of the listed statements is correct option is accurate because none of the other statements properly reflect how HyperDriveConfig and GridParameterSampling behave.

The experiment will produce trials for every numeric value in the 0.006 to 0.18 range for learning_rate is wrong because the code specifies discrete choices of 0.06, 0.12, and 0.18. It does not define a continuous range and 0.006 is not one of the provided choices.

The run will perform 60 trials for this hyperparameter search is wrong because max_total_runs is an upper bound and not a guarantee of run count. Grid sampling here yields 12 combinations so at most 12 trials will be created unless other sampling or limits change that number.

They can assign a security policy to the policy argument of HyperDriveConfig is wrong because the policy parameter is intended for early termination policies such as BanditPolicy or None. Security controls are managed separately in Azure Machine Learning and are not passed to HyperDriveConfig as the trial termination policy.

When evaluating hyperparameter tuning questions multiply the counts of discrete choices to get the total possible trials and remember that max_total_runs is only an upper bound and policy refers to early termination policies.

The correct answer is Ensemble models.

Ensemble models refer to approaches that combine many base learners to produce better predictive performance than single models. The description in the question that these constructs are not just one decision tree but a large number of trees fits ensemble methods such as random forests and gradient boosted trees. These methods are widely used because they often deliver strong predictions on complex data sets by averaging or aggregating the outputs of many trees.

Vertex AI is incorrect because it is a Google Cloud platform and managed service for building and deploying machine learning models rather than a specific model family made up of many trees.

Least squares regression is incorrect because it is a linear estimation method that fits a single linear model to minimize squared errors and it does not consist of many decision trees.

Linear regression is incorrect because it is a single parametric model that assumes a linear relationship between inputs and outputs and it does not match the description of an ensemble of trees.

Look for keywords like many trees, random forest, or boosting to identify ensemble methods. Also check whether the option is a platform or a specific model family before choosing your answer.

The correct answer is The endpoint accepts a batch of records in a single request and the payload may be JSON or CSV.

This option is correct because online prediction endpoints are designed to accept multiple instances in one HTTP request so clients can send a batch of records for throughput and efficiency. The endpoints accept both JSON and CSV formatted payloads when the data follows the service request schema for instances or inputs.

The endpoint accepts a single record per request and the payload may be JSON or CSV is incorrect because the service allows a batch of records in a single request rather than being limited to one record per call.

The endpoint only accepts multiple records in a single call and the body must be JSON is incorrect because although multiple records are accepted the payload is not restricted to JSON and CSV is also supported when formatted correctly.

The endpoint supports only one record per request and the body must be JSON is incorrect because the endpoint is not limited to a single record and it also accepts CSV formatted inputs in addition to JSON when following the expected request structure.

When testing online prediction endpoints send a batch of instances when possible and verify the exact request body format in the docs. Confirm whether the service expects JSON arrays or newline separated CSV rows before sending requests.

Enable Application Insights and associate it with the workspace and the deployed service is the correct choice.

Enabling Application Insights and associating it with the workspace and the deployed service lets Azure Machine Learning collect request level telemetry for the AKS hosted online endpoint. This captures request rates, latency, exceptions, and application logs and it lets you query traces and metrics in the portal for troubleshooting and performance analysis.

Application Insights integrates with the deployed service and the workspace so telemetry is correlated with the model deployment and you can view both high level metrics and detailed request traces without changing the cluster type.

Enable Azure Monitor for containers is incorrect because that feature focuses on node and container resource metrics and pod level performance rather than request level inference telemetry. It is useful for infrastructure monitoring but it does not provide the detailed application traces and request logs that Application Insights provides for model endpoints.

Redeploy the model to Azure Container Instances is incorrect because moving the deployment to ACI is not required to capture telemetry. ACI is typically used for testing or low scale scenarios and it does not substitute for enabling application level telemetry on the deployed service.

Move the AKS cluster into the same region as the Azure Machine Learning workspace is incorrect because relocating the cluster is unnecessary for telemetry collection. Application Insights and workspace association work across regions and there is no need to move AKS to collect inference logs and metrics.

When a question asks about collecting request level telemetry from a deployed model think of Application Insights and check whether it is associated with both the workspace and the deployed endpoint.

The correct answer is Unsupervised learning.

K-Means is a clustering algorithm that groups examples based on similarity without using labeled outputs. Because it discovers structure from unlabeled data, it is an instance of Unsupervised learning. Clustering methods like K-Means aim to partition data into cohesive groups and they do not require ground truth labels during training.

Reinforcement learning is about an agent learning to take actions to maximize cumulative rewards over time. It is not a clustering technique and does not describe what K-Means does, so it is incorrect.

K Nearest Neighbors is an instance based supervised method used for classification and regression that relies on labeled examples. It is not a clustering algorithm and therefore it is not the right category for K-Means.

Supervised learning involves learning a mapping from inputs to known outputs using labeled training data. Since K-Means does not use labels during training, it does not fall under supervised learning and that option is incorrect.

When a question mentions grouping or clustering of data without labeled outputs look for unsupervised. If the problem refers to known labels look for supervised and if it mentions agents, actions, or rewards think reinforcement.

The correct option is Register the model in the workspace model registry.

Register the model in the workspace model registry is the right choice because it stores model artifacts and the associated metadata while creating explicit versioned records inside the Azure Machine Learning workspace. This approach supports reproducibility and lets Dr. Patel track different iterations, attach tags and descriptions, and reference specific versions when promoting or deploying models.

Deploy the trained model to an endpoint is incorrect. Deployment publishes a model for serving and does not by itself catalog or version experiment artifacts inside the workspace. You can deploy from a registered model but deployment is not the mechanism for persisting different experiment versions.

Use child runs to organize experiment trials is incorrect. Child runs help structure and compare trials and they record metrics and outputs but they do not provide a central, versioned model registry for persistent artifact cataloging and lifecycle management.

Enable Application Insights telemetry is incorrect. Application Insights collects telemetry and monitoring data for deployed services and it does not store model artifacts or provide versioned model management inside the workspace.

When you need to persist and version models register them in the workspace model registry and use consistent names and tags so you can locate specific iterations easily.

All Azure Questions are from my DP-100 Udemy Course and certificationexams.pro

The correct option is Apply L1 and L2 penalty terms during training and augment the training images.

L1 and L2 penalties act as weight regularizers that constrain model complexity and reduce the tendency to memorize training noise. L1 promotes sparsity and L2 discourages large weights so they help the model generalize better by limiting capacity in a principled way. Image augmentation increases the effective size and diversity of the training set so the model sees more varied examples and learns more robust features.

Using both regularization and augmentation addresses overfitting from two angles because the penalties control complexity and augmentation reduces variance by broadening the data distribution.

Add dropout layers and enable batch normalization is not the best choice because dropout and batch normalization can help in some cases but they do not replace explicit data augmentation and targeted weight regularization. Batch normalization mainly stabilizes learning and dropout can interact poorly with convolutional feature maps if applied without care.

Perform transfer learning with a pretrained backbone and freeze most layers is not correct because freezing most layers limits the model’s ability to adapt to the new dataset and does not directly solve overfitting on the validation set. Transfer learning can help when data are very limited but it should be combined with augmentation and regularization to prevent overfitting.

Increase the network capacity by adding a 1024 neuron dense layer and reduce the number of training examples is wrong because increasing model capacity while reducing training data will make overfitting worse. Larger networks can memorize the training set and fewer examples increase variance and degrade validation performance.

When a question asks how to reduce overfitting look for choices that either expand the effective training data or constrain model complexity and be cautious about answers that increase capacity or reduce data.

The correct option is Use SMOTE sampling mode.

Use SMOTE sampling mode creates synthetic examples of the minority class so the model can learn its patterns more effectively during training. It balances the training data without discarding majority examples and it helps reduce bias toward the majority class which improves recall for rare labels when compared to no resampling.

Stratified split sampling preserves the original class proportions when creating training and test sets so it maintains the imbalance rather than fixing it. It is useful for fair evaluation but it does not address the need to increase minority class representation for training.

Random undersampling can balance classes by removing majority examples but it discards information and can hurt model performance when the majority class contains useful variety. It is a valid technique in some situations but it is not as appropriate as SMOTE when you want to augment the minority class.

Random split sampling simply splits the dataset without regard to class distribution and does not solve class imbalance. It risks producing training sets with even fewer minority examples and it does not generate new minority samples.

When a question mentions imbalanced classes look for answers that change the training distribution with methods such as SMOTE rather than answers that only split the data.

The correct options are Use the RunDetails widget in a notebook by running RunDetails(pipeline_run).show() and Call pipeline_run.wait_for_completion(show_output=True) and watch the console output.

The Use the RunDetails widget in a notebook by running RunDetails(pipeline_run).show() option is correct because the RunDetails widget provides an interactive view in notebooks that shows pipeline and step status, linked logs, and metrics. It is designed for quick visual monitoring while a pipeline run is active and it updates as the run progresses.

The Call pipeline_run.wait_for_completion(show_output=True) and watch the console output option is correct because the PipelineRun class exposes a wait_for_completion method that blocks until the run finishes and streams run output and logging to the console when show_output is set to True. This is a simple and useful way to follow progress from a script or terminal.

Check metrics and logs from the Kubernetes cluster in Azure Monitor is incorrect because Azure Machine Learning pipeline runs are monitored through the Azure ML run APIs, widgets, and the Studio experience. The cluster level metrics in Azure Monitor do not provide the step level run details and linked logs that the AML run monitoring surfaces.

Open the Inference Clusters tab in Machine Learning Designer is incorrect because the Machine Learning Designer inference clusters view is not the place to observe Azure ML pipeline execution. Designer is a different authoring tool and the Inference Clusters tab does not show pipeline run progress or step logs for SDK started pipelines.

When you see questions about observing pipeline progress think about the SDK and notebook features that stream run information. The RunDetails widget and the wait_for_completion(show_output=True) call are the most direct ways to watch a pipeline from code or a notebook.

The correct option is Call the fit method on the LinearRegression instance with the feature matrix and the target vector.

The fit method is the scikit learn estimator API call that trains the model by estimating coefficients from the provided feature matrix and target vector. Calling fit on a LinearRegression instance updates the model parameters so that it can later make predictions.

The option Call the predict method with the training feature matrix and the training labels is incorrect because predict is used to generate predictions from an already trained model and it does not update or train the model parameters.

The option Invoke the corr method on the model object and supply the feature and target arrays is incorrect because estimators do not provide a corr method for training. Correlation functions are data analysis operations and are not how scikit learn estimators learn parameters.

The option Call the score method on the estimator and pass the training feature matrix and the target array is incorrect because score evaluates a trained model by returning a performance metric such as R squared. It does not perform training or change model parameters.

Remember that scikit learn follows the estimator pattern where fit trains the model and predict or score are used after training to infer or evaluate.

Open a terminal on the Azure Machine Learning compute instance is the correct action to take before cloning the repository.

You must open a shell on the compute instance because the repository will be cloned into that instance’s local file system and the clone command is executed from a terminal. Opening the terminal gives you direct access to run git clone, to check the working directory, and to perform any required local setup before pulling code.

Generate a new SSH key pair is not the immediate first step because you need a terminal to run the key generation command and to place the keys in the right location on the compute instance. Generating keys can be necessary later, but it is performed from the compute instance terminal.

Launch an Azure Cloud Shell session is incorrect because Cloud Shell runs in a separate ephemeral environment in Azure and does not operate directly on the compute instance’s local file system. Cloud Shell could be useful for other tasks, but it does not replace opening a terminal on the compute instance itself when you intend to clone into that instance.

Add the public SSH key to the remote Git hosting account is not the very first action because you must first access the compute instance to generate or locate the public key and then copy it. Also some repositories use HTTPS or personal access tokens instead of SSH, so adding a key may not be required at all before cloning.

Open the compute instance terminal first and verify the repository authentication method. If you need SSH keys generate them from that terminal and then add the public key to the remote account.

Randomized grid sweep mode is the correct choice for sampling many hyperparameter combinations while minimizing compute usage.

The randomized grid sweep samples configurations randomly across the search space and lets you control the number of trials, so you get wide coverage without running every combination and you reduce compute consumption compared with exhaustive strategies.

Random sampling is effective when the search space is large because random draws can find promising regions quickly and you can set a fixed trial budget or stop early to limit cost.

Measured grid sweep mode is not correct because it focuses on evaluating specific predefined grid points and is not intended to provide broad random sampling under a constrained trial budget.

Vertex AI Hyperparameter Tuning is not correct for this question because it is a separate managed GCP service for tuning models and not a sweep mode option inside Nexa Machine Learning Studio.

Exhaustive grid sweep mode is not correct because it evaluates every combination in the grid and therefore maximizes compute usage, which contradicts the goal of minimizing compute.

When deciding between sweep modes think about whether you need full coverage or a limited budget because randomized sweeps explore broadly with fewer trials while exhaustive sweeps consume the most compute.

The correct option is Spark component in a pipeline job.

A Spark component in a pipeline job lets Maya run distributed Spark processing so she can handle 2.4 TB of tabular data efficiently. It integrates with Azure Machine Learning pipelines to provide automated scaling, job orchestration, and reproducible runs while keeping resource management low.

Azure Databricks notebook is a powerful environment for Spark but it is separate from Azure Machine Learning and typically requires managing a Databricks workspace and cluster configuration which increases operational overhead compared to using a managed Spark component inside Azure ML pipelines.

Standalone Spark job could run large jobs but it lacks the pipeline orchestration and integrated experiment tracking that a pipeline component provides and it can increase the burden of managing jobs and dependencies.

Azure Machine Learning compute instance is intended for interactive development and not for large distributed Spark processing. A compute instance will not efficiently process multi terabyte datasets without setting up a distributed Spark environment.

When a question emphasizes scale and low operational overhead look for answers that mention managed Spark and pipeline integration within the platform.

Refactor code into smaller reusable functions to improve readability maintenance and testability and Strip out exploratory fragments and print statements to streamline the script for automation are correct.

Refactoring into small reusable functions separates concerns and reduces duplicated logic. Functions create clear inputs and outputs which improves readability and makes it much easier to write unit tests and to reuse parts of the code in pipelines or other services.

Stripping exploratory fragments and print statements removes noise and unintended side effects that can break scheduled runs. A streamlined script produces predictable outputs and is simpler to monitor and maintain when it runs as an automated pipeline.

Convert explanatory notebook markdown cells into inline comments inside the script is not ideal because long narrative explanations are better kept in external documentation or converted into docstrings. Inline comments can bloat the code and they do not preserve markdown formatting or rich examples that belong in documentation.

Add structured logging and comprehensive error handling to support monitoring and operations is a good operational practice but it is not the primary focus of this question. The immediate priorities when converting notebooks are to modularize logic and remove exploratory code so the script is clean and testable. Structured logging and robust error handling are appropriate next steps when the cleaned script is integrated into production pipelines.

When converting notebooks start by modularizing code into small functions and by removing exploratory fragments before adding operational features like extensive logging or error handling.

The correct option is Transforming and cleaning raw datasets into structured formats suitable for machine learning models.

This is what data wrangling means in a machine learning environment. It covers the inspection and cleaning of raw inputs and the conversion of types and formats so the data can be consumed by models. Typical tasks include parsing messy text, handling missing or inconsistent values, encoding categorical fields, reshaping and joining datasets, and creating features for modeling.

Splitting datasets into training testing and validation subsets is not data wrangling. That action is a separate step focused on evaluation and is usually performed after the data has been cleaned and prepared.

Managing storage governance and sharing of data among analysts and teams describes data governance and collaboration rather than the technical cleaning and transformation work that data wrangling entails.

Being identical to simple preprocessing tasks such as scaling and basic imputations is incorrect because preprocessing can be one part of wrangling but the broader process also includes discovery, complex transformations, feature engineering, and combining multiple sources.

When a question asks about preparing raw datasets for models pick the option that mentions cleaning and transforming data and not the choices about governance or only splitting the data. Remember that data wrangling is broader than basic preprocessing.

Use logging.info to emit a line like “AUC: ” plus the AUC value is correct.

HyperDrive detects metrics by scanning the run logs for named metric lines. Using logging.info writes the metric to the run output in a consistent way so the tuning service can parse the logged “AUC” line and record the numeric value as the primary metric. Keep the logged text simple and start it with the metric name followed by the numeric value so the parser can find it reliably.

Use a print statement to write “AUC: ” plus the calculated value is incorrect because plain prints can be less reliable across execution environments and logging setups. The logging module produces structured output that is more consistently captured in the run logs.

Only save the numeric AUC to a file under outputs is incorrect because HyperDrive does not automatically scan output files for the primary metric during tuning. Writing the metric to a file will not make it visible to HyperDrive unless you also log that value to the run output.

Call Run.get_context and use run.log with the metric name and value is incorrect for this question because HyperDrive detects the primary metric from the run logs. While run.log can record metrics in the experiment store it is not the mechanism that HyperDrive parses for the primary metric in the estimator log parsing flow.

When you need HyperDrive to pick up a metric log a clear line to standard output such as “AUC: 0.92” using the logging module so the tuning job can parse it reliably.

The correct option is Classification.

Classification is the supervised machine learning task that assigns items to discrete categories or classes. In the clinic example a model learns from patient attributes and predicts whether a person falls into the class that has diabetes or the class that does not have diabetes.

Multinomial is not the general task name. It usually refers to a probability distribution or to multinomial logistic regression which is a specific technique for multi class problems rather than the overall type of learning called classification.

Binomial refers to a distribution or to scenarios with two possible outcomes and it is not the name of the machine learning task that assigns items to classes.

Logistic regression is an algorithm commonly used to perform classification but it is not the category of machine learning itself. The question asks for the task type which is classification.

Redundancy is not related to assigning items to categories. It generally means duplication or backup and is not a machine learning category.

Ordinal describes ordered categories and can be a subtype of classification when classes have a natural order but it is not the general term the question is asking for.

When a question asks for the type of machine learning focus on task names like classification or regression rather than on specific algorithms such as logistic regression.

Apply random sampling across the parameter domain to achieve broad coverage with moderate compute is correct.

Apply random sampling across the parameter domain to achieve broad coverage with moderate compute provides broad coverage of the hyperparameter space while letting you limit the total number of trials to fit your compute budget. Random sampling is simple to implement and it tends to find good configurations faster than exhaustive strategies when the space is high dimensional because it does not waste trials on regular intervals. It also avoids the modeling overhead of adaptive methods when you need straightforward, budget conscious exploration.

Use Bayesian optimization to model the performance surface and iteratively propose promising configurations is not chosen here because Bayesian optimization introduces extra complexity and overhead to build a surrogate model and it works best when evaluations are very expensive and you can run many sequential iterations. If you have a tight budget and you want broad coverage quickly then Bayesian methods may not be the most practical choice.

Narrow the parameter ranges and tune only a couple of variables to speed up experiments is not ideal because it risks missing important regions of the search space and it can overlook interactions between parameters. Reducing ranges and dimensions can be useful as a follow up strategy after an initial broad search but it does not by itself provide the broad coverage that the correct option gives.

Run an exhaustive grid search over all parameter combinations regardless of compute constraints is not appropriate because grid search scales exponentially with the number of parameters and quickly becomes infeasible on limited compute. An exhaustive grid may guarantee coverage of a fixed grid but it is very inefficient for high dimensional problems and for large ranges.

When you have a limited evaluation budget prefer random sampling to get broad coverage and set a fixed number of trials up front to control costs.

The correct option is off.

Choosing off tells Azure Automated Machine Learning to skip its built in featurization so the dataset is used exactly as provided and no automatic transformations are applied during experimentation.

This option is appropriate when the data has already been cleaned and prepared and you want to guarantee the same input features across runs or when you manage preprocessing outside of AutoML.

custom is incorrect because custom featurization allows you to supply specific featurization rules or a featurization configuration and AutoML will apply those transformations which can change the dataset.

auto is incorrect because auto featurization enables Automated ML to detect and apply transformations automatically and that will modify the input data to create features.

manual is incorrect because manual featurization implies the process will follow user selected feature processing steps which can also alter the original dataset rather than leaving it unchanged.

When you need to preserve the original dataset preprocess the data before submitting it to Automated ML and set featurization to off so experiments run on unchanged inputs.

The correct option is A Python entry script and an environment specification.

An MLflow project needs a concrete entry point so that the framework knows what code to execute and with what parameters. The Python entry script is the runnable code that defines the experiment or training job and it must be present so the project can actually run.

Reproducibility depends on capturing the execution environment. The environment specification records exact dependency versions and can be a Conda environment file or a container definition so the same software stack can be recreated across machines and over time.

A registered model artifact and its evaluation metrics is incorrect because a registered model and metrics are outputs of an experiment and not the assets that define how to run the project or reproduce the run. They document results but they do not specify the code or the environment needed to execute the task.

A Docker image and a model registry entry is incorrect because a Docker image alone can represent an environment but a model registry entry is a storage artifact rather than a runnable entry point. The project still needs the actual entry script or defined entry point so the code can be invoked consistently.

An MLproject descriptor file and a dataset snapshot is incorrect because while the MLproject descriptor is useful for describing project metadata and entry points, it does not replace the need for the actual runnable script and a pinned environment. A dataset snapshot can help with reproducibility when data must be frozen but it is not one of the two core assets required to make the project executable and fully reproducible.

When the exam asks about reproducibility think about what you must have to run the code and to recreate the software stack. Pay attention to options that pair a runnable entry point with a pinned environment and treat artifacts or metrics as results rather than requirements.

All Azure Questions are from my DP-100 Udemy Course and certificationexams.pro

0 3 * * * is correct. This cron expression sets minute to zero and hour to three so it runs once every day at 3 00 AM which matches the required daily cleaning task.

Cron expressions used for Azure Machine Learning schedules list fields left to right as minute then hour then day then month then weekday. The expression 0 3 * * * therefore triggers at minute 0 of hour 3 every day which is the expected daily 3 00 AM run time.

frequency=”hour”, interval=3 is incorrect because that configuration means run every three hours. It does not schedule a single daily run at a specific hour so it will not ensure the cleaning task runs only at 3 00 AM each day.

3 0 * * * is incorrect because the fields are minute then hour. That expression runs at minute 3 of hour 0 which is 00 03 AM, not 03 00 AM, so it does not match the needed time.

frequency=”day”, interval=2 is incorrect because the interval of two days causes the pipeline to run every other day. That does not produce a daily cleaning run at 3 00 AM as required.

Read cron fields from left to right as minute, hour, day, month, and weekday to quickly verify the intended run time.

The correct answer is Call the Run.log and Run.log_list methods on the Run object to record named metrics for each run.

The Run.log and Run.log_list methods are designed to record structured metrics into the Azure Machine Learning run history so each metric is stored with a name and timestamp and can be queried efficiently later. Using these methods lets the team record single values and lists of values in a way that the SDK, the REST API, and the Azure ML studio can all retrieve and visualize for downstream analysis.

Rely on print statements in the script to emit metrics to stdout is incorrect because printed output is unstructured and is not captured as named metrics in the run history, which makes querying and analyzing metrics across runs unreliable.

Send metrics to Application Insights and query them later is incorrect for this scenario because it requires extra telemetry setup and it is not the standard or most efficient way to track experiment metrics with the Azure ML Python SDK. The SDK provides native logging into the run history which is simpler and more directly supported.

Write metric files into the run outputs folder for later download is incorrect because saving files requires manual parsing and download to aggregate metrics across runs, and it does not provide the efficient, queryable metric storage that Run.log and Run.log_list offer.

When you see questions about experiment metrics on the exam think about the run history and the SDK methods for logging. Prefer Run.log and Run.log_list for structured and queryable metrics rather than relying on prints or files.

The correct answer is Google Kubernetes Engine.

Google Kubernetes Engine provides a managed container orchestration platform that lets you control node types, attach GPUs, configure node pools and tune networking for consistent low latency. It supports autoscaling, health checks and rolling updates while keeping cold starts predictable which makes it a good choice for hosting low latency online model endpoints for real time inference.

Cloud Run is a serverless container platform that is great for stateless HTTP workloads but it can introduce cold starts and it gives less control over node level resources which can make strict low latency requirements harder to meet.

Google Compute Engine offers raw virtual machines that can be tuned for latency but it lacks built in container orchestration and automatic rolling updates which increases operational overhead when you need scalable, container based model serving.

On premise servers can be optimized for latency but they add management burden and reduce the benefits of cloud managed services for scaling, availability and integration with cloud AI and monitoring tools.

When a question emphasizes low latency and real time inference choose a service that gives you control over instance types, container orchestration and scaling rather than a purely serverless option.

The correct answer is Use Quantiles normalization together with QuantileIndex normalization.

Use Quantiles normalization together with QuantileIndex normalization is correct because the process first determines quantile cut points and then produces discrete bin indices. Quantiles normalization computes the boundaries so that bins have roughly equal frequency and QuantileIndex normalization applies those boundaries to output an integer or categorical index for each row. The resulting indexed bins are directly usable as a predictor in a model.

Use the Bin Data module with manually specified bin edges is not the best match for the editor’s instruction because manually specifying edges bypasses quantile computation and it does not follow the requested Quantiles plus QuantileIndex approach.

Normalize values using ZScore and then apply equal width binning is incorrect because ZScore standardization changes scale but does not produce quantile boundaries. Equal width bins after ZScore will not produce equal frequency bins and so they will not match a quantile based binning strategy.

Apply Quantiles normalization by itself without creating a QuantileIndex mapping is incorrect because Quantiles alone only computes the mapping or transforms the distribution. Without the QuantileIndex step you do not get the discrete bin index column needed for use as a predictor.

When a question asks about producing binned predictors look for a two step approach that both computes bin boundaries and assigns indices. Remember that normalization is not the same as binning and exams often test the distinction.

Directory asset is the correct option for registering a publicly available collection of image files because it allows you to point to and manage multiple files together as a single logical dataset.

A Directory asset represents a folder or prefix of files and so it is efficient for organizing many images for training or labeling. It can reference a public URL or a cloud storage prefix and preserves the grouping of files so you can import or process them together instead of handling each image individually.

Tabular asset is intended for structured row and column data like CSV files or tables and not for collections of image files, so it does not fit this use case.

Stream recording asset is meant for time series or streaming media recordings and it does not provide a natural way to group static image files for machine learning.

Single file asset points to one file and is suitable only when the entire dataset is contained in a single archive or file, so it is not efficient for a directory of many separate image files.

When deciding asset types first determine whether your dataset is a single file or a collection. Use Single file asset for one file and use Directory asset when you need to register many files together.

The correct answer is Deploy the environment on Azure Databricks and orchestrate pipelines with Azure Data Factory.

Azure Databricks natively supports Python and Scala and provides collaborative notebooks for interactive sessions and experiment work. It runs Apache Spark on managed clusters so it can scale across a cluster of machines and it supports workload isolation through separate clusters or job clusters and cluster pools.

Azure Data Factory provides an automated orchestration layer that can design and run pipelines which move and process data and it integrates directly with Databricks to trigger notebooks and jobs. This combination gives a single orchestration solution for both data engineering and data science workflows while handling storage movement and processing.

Build the solution using Hive on an HDInsight cluster and coordinate pipelines with Azure Data Factory is not ideal because Hive on HDInsight is an older batch focused platform. It does not provide the same native interactive notebook experience and collaborative data science features that Databricks offers and it is less suited for mixed interactive and automated workloads.

Run workloads on Azure Databricks and use Azure Container Instances for orchestration is not a good match because Azure Container Instances does not provide the robust pipeline orchestration scheduling dependency management and integration that Azure Data Factory provides. Using ACI would require building custom orchestration which increases complexity.

Create an Apache Spark environment on HDInsight and use Azure Kubernetes Service to orchestrate workflows can be made to work but it adds significant operational overhead and complexity. Managing Spark on HDInsight and coordinating workflows on AKS lacks the seamless integration for notebooks jobs and pipeline orchestration that Databricks plus ADF deliver and HDInsight is less commonly recommended for modern data science platforms.

Choose options that mention managed Spark notebooks and a dedicated orchestration service when the scenario needs both interactive data science and automated pipelines.

You can create VM instances with the Python client library is correct because Google Cloud provides Python client libraries that let you create and manage Compute Engine virtual machine instances programmatically.

The You can create VM instances with the Python client library option is correct because the Compute Engine Python client or the Google Cloud Python libraries call the underlying REST API to create, configure, start, stop and delete instances. These libraries use application default credentials or service account credentials to authenticate and they expose the same functionality available via the REST API so you can perform full lifecycle management of VMs from Python.

gcloud CLI is incorrect because that item names the command line tool rather than the Python client library. The gcloud CLI can create VM instances but it is not a Python library and the question asks specifically about using the Python client.

Compute Engine REST API is incorrect in this context because the REST API is the underlying interface that client libraries use. The REST API is a valid way to create VMs but the option does not answer whether the Python client library itself can create instances. The Python client simply makes REST calls on your behalf.

No the Python client is only for data preparation model training and deployment is incorrect because Python client libraries cover many Google Cloud services including Compute Engine. The Python client is not limited to machine learning workflows and it can manage VM resources as well.

When a question asks what a client library can do remember that client libraries are language bindings for the underlying APIs and they can perform the same operations as the REST API. Pick the option that explicitly names the library or client when the question targets a specific language.

The correct answer is predictions = NER_Model.transform(validationData).

You use predictions = NER_Model.transform(validationData) because the DataFrame based machine learning API applies a trained model to a dataset with transform and returns a DataFrame with prediction columns. The model has already been trained so running transform on the validationData produces the predicted values you need to assess performance.

predictions = NER_Model.convert(trainingData) is incorrect because there is no standard model method named convert in the common DataFrame based ML APIs. That call would not produce predictions and it does not represent the usual training or inference step.

predictions = NER_Model.fit(trainingData) is incorrect because fit is used to train or fit a model to data and it does not return predictions for a validation set. Calling fit on trainingData would produce a trained model rather than predicted outputs for validationData.

predictions = NER_Model.predict(validationData) is incorrect in this context because the DataFrame based API expects transform to produce predictions from a DataFrame. Some libraries offer a predict method for array based inputs but that is not the correct call when working with a DataFrame based model and a validation DataFrame.

When a model has been trained and you have a validation DataFrame remember to use transform to generate predictions and keep your validation set separate to avoid data leakage.

Automatically search for top performing algorithms and hyperparameter settings for a given problem is the correct choice.

AutoML systems perform automated experimentation across different model types and hyperparameter configurations and then surface the best performing candidates for a given dataset and objective. This automation reduces the need for deep data science expertise because it handles model selection, tuning, and ranking of results on behalf of the practitioner.

Catalog of ready made pretrained models built from public datasets is incorrect because that option describes a model catalog or hub rather than the core capability of AutoML. AutoML focuses on searching and tuning models for your specific problem and dataset rather than simply offering a library of pretrained models.

Validate newly created custom pipeline components is incorrect because validation of custom components is an engineering and testing activity. AutoML may integrate into pipelines but its main benefit is automated model and hyperparameter search rather than component validation.

Vertex AI is incorrect because it is a platform name and not a benefit. Vertex AI can provide AutoML features but the question asked for a significant benefit of an AutoML solution and not for the name of a platform.

When a question contrasts helping non experts assemble end to end pipelines with another benefit look for options that mention automated model selection or hyperparameter tuning as those describe core AutoML advantages.

The correct options are An R squared value near one and A low Root Mean Squared Error value.

An R squared value near one indicates that the regression model explains a large proportion of the variance in trip fares which means the model captures the relationships in the data well. R squared is a relative measure of explained variance and values closer to one reflect a better fit for continuous targets like fare amounts.

A low Root Mean Squared Error value means the average magnitude of the prediction errors is small and the errors are measured in the same units as the target so a lower RMSE directly corresponds to predictions that are closer to actual fare values. RMSE also penalizes larger errors more strongly which is useful when big prediction mistakes matter.

A large Root Mean Squared Error value is incorrect because a large RMSE signals large prediction errors and therefore poor regression performance which is the opposite of what you want when evaluating a fare prediction model.

A high F1 score is incorrect because the F1 score measures precision and recall for classification tasks and it does not apply to regression problems with continuous targets like trip fares.

An R squared value close to zero is incorrect because a value near zero means the model explains almost none of the variance in the target and therefore performs poorly at predicting fares.

A low F1 score is incorrect because a low F1 indicates poor classification performance and it is not relevant for evaluating a regression model.

When you see a question about predicting continuous values check first that the task is regression and then prefer metrics like R squared and RMSE rather than classification metrics such as F1.

Replace [A] with KFold replace [B] with 3 and replace [C] with data

KFold with n_splits set to 3 produces three folds where each test fold contains two samples and each training fold contains four samples when you start from a six element array. You use the array passed as the first argument to the KFold.split method so replacing [C] with the variable data is correct for generating the train and test indices.

KFold does not require labels or group information and it is the appropriate cross validation class when you want deterministic k fold indices from the input order or when you explicitly shuffle and set a random state. Setting n_splits to 3 yields the required fold sizes that match the described training and test sizes.

Replace [A] with StratifiedKFold replace [B] with 3 and replace [C] with data is incorrect because stratified folds require class labels to preserve class proportions and the question provides only a single feature array without labels.

Replace [A] with ShuffleSplit replace [B] with 3 and replace [C] with data is incorrect because ShuffleSplit creates random independent train test splits and those test sets can overlap and will not produce the deterministic non overlapping fold structure that k fold produces.

Replace [A] with KMeans replace [B] with 6 and replace [C] with train is incorrect because KMeans is a clustering algorithm and not a cross validation splitter, and it will not produce train and test index splits.

Replace [A] with GroupKFold replace [B] with 3 and replace [C] with data is incorrect because GroupKFold requires a groups array to ensure samples from the same group are kept together across folds, and no group labels are provided in this scenario.

Replace [A] with cross_validate replace [B] with 3 and replace [C] with array is incorrect because cross_validate is a convenience function for evaluating an estimator and it is not a splitter object to replace the placeholders in code that expects a cross validation splitter like KFold.

When you see a question about producing k folds with specific train and test sizes look for KFold and the n_splits parameter. Remember that stratified or group splitters need labels or group arrays and shuffle based splitters are random unless you set a random seed.

All Azure Questions are from my DP-100 Udemy Course and certificationexams.pro

The correct answer is The output layer should contain three neurons.

The output layer should contain three neurons because this is a three class classification problem and the output layer normally has one neuron per class. Using three output neurons with a softmax activation lets the network produce a probability for each class so the model can select the most likely category.

Because there are 12 numeric features the input layer should have 12 inputs and typically 12 neurons when using a dense input layer. The input size must match the number of features unless you perform an explicit feature engineering or dimensionality reduction step.

Vertex AI is incorrect because it is a Google Cloud platform for building deploying and managing ML models and it does not specify the neuron counts or architecture of a particular network. The option names a service and not an architectural requirement.

The input layer should have three neurons is incorrect because the problem states there are 12 numeric features and the input layer must accommodate those features. Choosing three input neurons would discard or combine information and would not match the described input shape.

The input layer should contain six neurons is incorrect for the same reason because six does not match the twelve features that are provided and the question does not mention any preprocessing that would reduce the feature count to six.

Count the number of features to set input neurons and count the number of classes to set output neurons. Use softmax for mutually exclusive multiclass classification.

Azure Machine Learning Python SDK is correct. Elena should use the Azure Machine Learning Python SDK to submit her training script from her local Python environment and have the run and metrics recorded in Azure Machine Learning.

The Azure Machine Learning Python SDK provides a programmatic Python API to create experiments or jobs configure compute targets upload datasets submit training runs and stream logs back to the Azure Machine Learning workspace. The SDK integrates with the workspace so Elena can launch training from her preferred IDE while still using the Azure Machine Learning tracking, model registry and compute resources.

Azure HDInsight is not correct because it is a managed Hadoop and Spark service for big data processing and not the Python API used to submit and track Azure Machine Learning experiments.

Azure CLI is not correct for this scenario because it is a command line tool for managing Azure resources and while it can perform some Azure Machine Learning tasks it is not the native Python library that a data scientist would use from their Python development environment to run and monitor experiments.

Azure Machine Learning studio is not correct because it refers to the web based studio and designer interfaces for building and monitoring experiments and not the Python package Elena would use to execute a training script from her local Python environment.

When a question asks about running code from your local Python environment choose the tool that provides a native Python API. The Azure Machine Learning Python SDK is the typical answer for submitting and tracking experiments programmatically.

The correct answer is Data Science Virtual Machine for Linux (Ubuntu).

The Data Science Virtual Machine for Linux (Ubuntu) image is the best choice because open source deep learning frameworks such as Caffe2 and PyTorch have the broadest and most mature support on Linux and especially on Ubuntu. The image includes easy access to GPU drivers, CUDA and cuDNN and it is commonly used for automated package installs with conda and pip which simplifies configuring deep learning environments.

The Data Science Virtual Machine for Linux (Ubuntu) edition also receives more frequent community and vendor testing for popular deep learning stacks which makes compatibility and driver support more reliable for training and inference workloads.

Data Science Virtual Machine for Windows 2018 is not ideal because Windows has more limited native support for many open source deep learning toolchains and GPU driver setups are generally more straightforward on Linux.

Geo AI Data Science Virtual Machine with ArcGIS is specialized for geospatial and ArcGIS workloads and it is tailored with Esri software that is not necessary for general deep learning tasks.

Data Science Virtual Machine for Linux (Debian) can run deep learning libraries but the exam and vendor images typically emphasize Ubuntu as the most supported distribution for PyTorch and Caffe2 which is why Ubuntu is the preferred choice.

Data Science Virtual Machine for Windows 2014 is an older Windows image and it shares the same limitation of Windows platforms being less commonly used for native builds and driver compatibility with these open source frameworks.

When a question asks for maximum compatibility with open source deep learning libraries favor Linux images and especially Ubuntu because they offer the most straightforward GPU driver and library support.

The correct option is from azure.ai.ml.dsl import pipeline.

This import brings in the pipeline decorator from the Azure Machine Learning SDK v2 domain specific language and it is the supported way to define DSL pipelines in Python. The decorator wraps a Python function and turns it into a pipeline job that you can submit with an MLClient.

from azure.ai.ml import pipeline is incorrect because the top level azure.ai.ml package does not provide the DSL pipeline decorator at that location in SDK v2. You need to import the decorator from the dsl submodule.

azure.pipeline is incorrect because it is not a valid Azure ML SDK v2 import path and it does not expose the pipeline decorator.

azure.ai.ml.dsl.pipeline is incorrect as presented because it is a dotted module path rather than a proper import statement that brings the decorator into scope. The correct form uses the from import syntax.

azureml.pipeline.core.Pipeline is incorrect because it belongs to the older Azure ML SDK v1. That class is part of the legacy SDK and is not the recommended way to define pipelines in SDK v2. Deprecated v1 APIs are less likely to be the correct answer on newer exams that target SDK v2.

When a question mentions Azure Machine Learning SDK v2 look for imports under azure.ai.ml and especially the azure.ai.ml.dsl namespace which contains the pipeline decorator and the DSL constructs used to define pipeline jobs.

The correct answer is: Custom roles let you define allowed actions and explicit deny rules so you can grant and restrict access to specific workspace resources.

Custom roles let you assemble a precise set of permissions so you can follow least privilege and assign only the actions that a user or service account needs. You can use custom roles together with IAM deny policies when you need to enforce explicit denies that block access even if some allow permissions exist. This combination makes it possible to grant and restrict access to specific workspace resources in a machine learning environment.

Cloud IAM predefined roles manage access at the project level is incorrect because predefined roles are managed by Google and they can be applied at organization, folder, project, or resource levels and they differ from primitive roles.

Custom roles change the synthesized voice used by services is incorrect because IAM roles control permissions and not service configuration such as synthesized voice, which is handled by the specific API settings.

Custom roles restrict a user to view only operations within a workspace is incorrect because a custom role can include any combination of permissions and it will only restrict a user to viewing operations if you explicitly grant only viewing permissions.

When you read questions about roles focus on whether the option describes who manages roles or what permissions are granted. Use custom roles for fine grained permission sets and remember that explicit denies are implemented with IAM deny policies.

Bayesian sampling can be paired with an early stopping policy is correct.

Bayesian sampling means using Bayesian optimization to model the relationship between hyperparameters and the objective and it can be combined with early stopping policies to terminate unpromising trials early and save compute and time. Early stopping policies monitor intermediate metrics and stop trials that are unlikely to outperform the best candidates so the search can focus on more promising configurations.

Bayesian sampling is restricted to only uniform choice and quniform parameter types is incorrect because Bayesian approaches accept a wide range of parameter types including categorical, integer, continuous, log scaled, and conditional parameters and they are not limited to only uniform or quniform choices.

Bayesian sampling always finds the absolute best configuration and is the slowest approach is incorrect because Bayesian optimization is a sample efficient search strategy but it does not guarantee finding the global optimum and runtime depends on how expensive each trial is so it is not inherently the slowest method in all situations.

Vertex AI Vizier is incorrect as an answer in this context because that option is just the name of a service rather than a property of Bayesian sampling and the question asked about what is true about Bayesian sampling rather than about a specific product.

Focus on capability words when reading hyperparameter tuning questions and watch for absolute claims such as always or only which are usually incorrect.

All Azure Questions are from my DP-100 Udemy Course and certificationexams.pro

get_run() is the correct option. When used with the MLflowClient it returns the Run object that contains the run data and the metrics, so you can read the metrics inside your Jupyter notebook.

The get_run() call returns a run object whose data attribute exposes the metrics that were logged during the job. You can inspect the returned object in Python and read values from run.data.metrics or iterate the metrics to use them in analysis or visualizations.

log_artifact() is incorrect because that method uploads or logs files and artifacts to a run and does not return metric values. It stores files such as models or logs rather than retrieving metrics.

get_metric_history() is incorrect for this scenario because it requires a specific metric name and returns the historical entries for that single metric instead of returning the run object with the full set of metrics. It is useful to see metric trends but it does not directly give you the run metrics object.

log_metric() is incorrect because that method writes a metric value to a run rather than reading metrics. It records or updates metrics and does not provide a way to fetch existing metric values.

When working in a notebook call get_run() to retrieve the Run object and then inspect run.data.metrics to access the logged metrics. Remember that logging methods write data while get methods return the recorded values.

The correct answer is ManagedOnlineDeployment.

The ManagedOnlineDeployment class represents a single deployment of a model to an online endpoint and is the object you use when you need to declare instance type, instance count, the runtime environment, and the code or scoring configuration for serving predictions. This class lets you specify compute properties and container or environment settings and it is attached to an endpoint to run online inference.

NetworkSettings is incorrect because it is used to control networking and access rules for resources and not to define the instance type, runtime environment, or scoring code for a deployment.

Model is incorrect because it represents a registered model artifact rather than a deployment. You reference a model inside a deployment, but you do not use the Model class to configure the runtime or compute for an online service.

OnlineEndpoint is incorrect because it represents the endpoint resource that receives and routes traffic. You create an endpoint and then attach one or more deployments to it, but the endpoint itself does not set the instance type, runtime, or code for a specific deployment.

When a question asks which class sets machine type, runtime, and scoring code for a live online service look for the class that includes the word Deployment. The OnlineEndpoint manages traffic and the Model is the artifact you reference from the deployment.

The correct answer is One.

The coefficient of determination measures the proportion of variance in the target that the model explains and therefore values closer to 1 indicate the model explains more of the variance and has stronger predictive performance. A higher R squared means the model predictions align better with the observed values while a lower value means the model explains less of the variability.

Explained variance score is a related metric that quantifies explained variance but it is the name of a metric and not the numeric target that indicates stronger predictive performance in this question. It is not the correct choice.

Zero would indicate that the model explains none of the variance and therefore it represents poor predictive performance. The question asks which number closer to indicates stronger performance so Zero is the opposite of the correct direction.

The mean value of the label is the simple baseline that R squared compares against when assessing improvement but the question asks which number R squared being closer to indicates stronger predictive performance and that number is One. Predicting the mean is the baseline that yields an R squared near zero.

The correlation coefficient measures linear association between two variables and in simple linear regression its square corresponds to R squared. The coefficient itself is a different statistic and can be negative so it is not the numeric answer the question seeks.

Remember that R squared expresses the proportion of variance explained and values closer to 1 indicate a better model fit.

The correct answer is Compute refers to the underlying virtual or physical resources that run workloads and Environment encapsulates the software dependencies and runtime needed to execute the code.

Compute denotes the actual machines or clusters that provide CPU, memory, networking and accelerators like GPUs. These resources allocate capacity, schedule jobs and host processes so they are responsible for execution performance and scaling.

Environment describes the operating system, libraries, language runtimes and container image or dependency specification that the code needs. Environments make runs reproducible and portable by packaging the software side of the workflow separately from the hardware.

An Environment is the hardware specification that hosts Computes while Computes are the software images that execute the workload is incorrect because it reverses the roles. Hardware and resource provisioning are the responsibility of compute, and software images or dependency bundles belong to the environment.

A Compute cluster is capable of spanning multiple nodes whereas an Environment is restricted to a single execution instance is incorrect because an environment is not inherently limited to one instance. An environment can be used across distributed or multi node jobs and across many instances to ensure the same runtime and dependencies everywhere.

An Environment is equivalent to an image registry and Compute simply pulls images from that registry to run tasks is incorrect because a registry is a storage location that holds container images while an environment is the contents or specification of the runtime itself. Registries and artifact stores are separate infrastructure components from the environment concept.

When deciding between options focus on whether the statement assigns hardware responsibilities or software dependencies. Remember that Compute maps to machines and capacity and Environment maps to runtimes and libraries.