Free AWS AI Practitioner Test Simulator Covering all Exam Topics

EduStream Labs is building a model to organize a library of about 1.3 million lecture clips. Some clips should receive several topic tags such as Algebra and Probability, while others should be placed into only one difficulty level such as Beginner or Advanced. The team wants to pick the right approach by understanding how multi-class differs from multi-label classification. Which statement best describes the difference?

• ❏ A. Multi-class does not require labeled training data, but multi-label requires labeled examples

• ❏ B. Multi-class enables an item to match many categories at once, whereas multi-label restricts the item to a single category

• ❏ C. Multi-class assigns each example to exactly one class, while multi-label allows the same example to belong to multiple classes

• ❏ D. Amazon Comprehend

A digital payments startup is training machine learning models to detect transaction fraud and assess lending risk. Because these use cases are nuanced, the team wants human reviewers involved at key points to label data and validate outputs so the models remain accurate and trustworthy. Which AWS service should they use to integrate human feedback directly into the model development workflow?

• ❏ A. Amazon SageMaker Clarify

• ❏ B. Amazon SageMaker Feature Store

• ❏ C. Amazon SageMaker Ground Truth

• ❏ D. Amazon SageMaker Role Manager

Northstar Lending plans to roll out a credit risk model and wants to check for potential bias in both the training data and predicted outcomes before going live. Which AWS service should they use to run this bias assessment?

• ❏ A. Amazon Comprehend

• ❏ B. AWS Glue DataBrew

• ❏ C. Amazon SageMaker Clarify

• ❏ D. Amazon SageMaker Model Monitor

A kids media startup named WillowWorks Edu is building an interactive storytelling app that invents new narratives inspired by beloved folk tales for children ages 5 to 9. The team must enforce safety rules so every generated story remains age appropriate and avoids harmful material. Which AWS capability best meets this requirement?

• ❏ A. Amazon Rekognition

• ❏ B. Guardrails for Amazon Bedrock

• ❏ C. Agents for Amazon Bedrock

• ❏ D. Amazon Bedrock playgrounds

A helpdesk platform at NorthShore Retail ingests about 120,000 log lines each day, and intermittent database issues leave many log sentences partially cut off. The team wants to apply machine learning to automatically propose the missing words to speed up troubleshooting. Which model approach best suits this requirement?

• ❏ A. Prescriptive analytics model

• ❏ B. Amazon Comprehend

• ❏ C. BERT-based masked language model

• ❏ D. Unsupervised clustering model

A regional telecom provider wants to gauge customer sentiment from its call center recordings. The company stores about 90,000 minutes of audio per month in Amazon S3 and needs a fully managed approach that converts speech to text and then determines sentiment from that text. Which AWS services should the team use together to meet this goal?

• ❏ A. Amazon Polly and Amazon Comprehend

• ❏ B. Amazon Transcribe and Amazon Translate

• ❏ C. Amazon Transcribe and Amazon Comprehend

• ❏ D. Amazon Rekognition and Amazon Transcribe

NorthBay Finance just imported a fresh dataset with roughly 180 columns. The team calculated summary statistics, created histograms and box plots, and generated a correlation heatmap to identify relationships. Which phase of the machine learning workflow are they performing?

• ❏ A. Data preprocessing

• ❏ B. Exploratory data analysis

• ❏ C. Hyperparameter tuning

• ❏ D. Feature engineering

A regional hospital network wants to build predictive models on AWS using 24 months of de-identified clinical notes and lab summaries to flag upcoming patient risk events. The data science team is comparing AWS services that can support this predictive healthcare analytics effort. Which statements correctly reflect AWS services that are appropriate for this need? (Choose 2)

• ❏ A. Amazon Kendra evaluates medical documents to directly predict future patient conditions

• ❏ B. Amazon SageMaker provides a managed environment to build, train, and deploy models that estimate patient risk scores

• ❏ C. Amazon Polly predicts clinical outcomes by converting text content into speech

• ❏ D. Amazon Comprehend Medical identifies medical entities in unstructured patient notes that can be transformed into features for predictive models

• ❏ E. Amazon Rekognition determines patient diagnoses from images of charts and scans

A regional bank is building machine learning systems to flag potentially risky transactions for compliance reviews. The team is comparing a very complex, high-accuracy model with a more interpretable model that clearly explains how each decision is reached. Because auditors review decisions every 90 days, the engineers want to prioritize trust, auditability, and accountability. Which benefits would most likely lead a developer to favor a transparent, explainable model? (Choose 2)

• ❏ A. They lower infrastructure and storage requirements

• ❏ B. They make debugging and performance tuning more straightforward

• ❏ C. AWS Key Management Service

• ❏ D. They increase stakeholder trust by making decisions understandable

• ❏ E. They strengthen security by obscuring decision rules

A regional insurance provider wants to upgrade its cloud contact center with generative AI to help human agents. The company aims to reduce average handle time by 15 percent by generating context-aware replies, suggesting next-best actions during calls, and automating routine after-call work. They are comparing AWS offerings that can deliver these capabilities within their contact center environment. Which service best fits this need?

• ❏ A. Amazon Q Business

• ❏ B. Amazon Q in Connect

• ❏ C. Amazon Q in QuickSight

• ❏ D. Amazon Q Developer

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A data scientist at Alpine Bank uses an Amazon Bedrock base model to generate concise summaries from live help desk chats and needs to capture both requests and responses for audit and troubleshooting purposes without building custom pipelines. How should the team enable invocation logging?

• ❏ A. Configure AWS CloudTrail as the logs destination for the model

• ❏ B. Enable invocation logging in Amazon Bedrock

• ❏ C. Configure model invocation logging in Amazon EventBridge

• ❏ D. Send model logs directly to Amazon CloudWatch Logs

A retail startup plans to deploy a customer help chatbot using an Amazon Bedrock foundation model to answer product FAQs. The assistant must incorporate proprietary details from internal product manuals and policy PDFs stored in an Amazon S3 bucket. What is the most straightforward way to ground the model’s answers with this private content?

• ❏ A. Amazon Kendra

• ❏ B. Amazon Bedrock Knowledge Bases

• ❏ C. Amazon Bedrock Guardrails

• ❏ D. Amazon SageMaker training

A regional online retailer is building a customer support FAQ assistant on Amazon Bedrock using a knowledge base. They need concise and highly accurate answers and are targeting response times below 300 milliseconds. What combination of approaches will provide the most effective results? (Choose 2)

• ❏ A. BLEU metric tuning

• ❏ B. Retrieval-augmented generation (RAG)

• ❏ C. Model fine-tuning

• ❏ D. Prompt engineering techniques

• ❏ E. Multilingual model support

An AI research group at a telemedicine startup wants to create a machine learning model while avoiding any server setup or infrastructure management. Which Amazon SageMaker capability enables them to build, train, and deploy models in a single managed workspace?

• ❏ A. Amazon SageMaker Ground Truth

• ❏ B. Amazon SageMaker Studio

• ❏ C. Amazon SageMaker JumpStart

• ❏ D. Amazon SageMaker Autopilot

A regional asset manager, Solstice Capital, is using Amazon Bedrock to draft quarterly market commentary with foundation models. Over a three-week pilot, the data science team is experimenting with inference settings to balance novelty with factual precision and they are focusing on the Temperature parameter. What is the effect of changing the Temperature value?

• ❏ A. It enforces a hard cap on the number of tokens returned to keep responses short and uniform

• ❏ B. It sets a nucleus sampling threshold top p that restricts choices to a cumulative probability cutoff

• ❏ C. It modulates sampling randomness, making outputs more predictable at low values and more varied at higher values

• ❏ D. It removes all low-probability tokens so the model always chooses the single most likely next token

A digital publishing startup is prompting a foundation model through Amazon Bedrock to create marketing copy and wants to fine-tune how imaginative versus deterministic the text is for identical prompts. Which inference parameter should they adjust?

• ❏ A. Top K

• ❏ B. Temperature

• ❏ C. Top P

• ❏ D. Stop sequences

A regional insurance provider plans to use a large language model to automate the review of policy documents and wants to emphasize responsible design and rollout. Which actions should the company take? (Choose 2)

• ❏ A. Balance and de-bias the training corpus to reduce harmful skew

• ❏ B. Tune the temperature parameter to 0.2 for more consistent responses

• ❏ C. Add fairness metrics to the model evaluation process

• ❏ D. Use prompt engineering to coax the model toward desired formats

• ❏ E. Train longer to memorize domain terminology and reduce loss

A telehealth provider called Northwind Care is rolling out an AI-driven triage platform and wants service-to-service calls between its VPC applications and AWS service endpoints to remain private and not traverse the public internet. Which AWS service should they use to accomplish this?

• ❏ A. IAM policies

• ❏ B. AWS Macie

• ❏ C. AWS PrivateLink

• ❏ D. AWS Config

A digital publishing firm named NovaPage wants an AI assistant to help its 20-person engineering team accelerate coding, create and run unit tests, and modernize codebases such as migrating frameworks and languages. The team needs something that integrates with IDEs and automates repetitive developer tasks to increase productivity. Which AWS service best supports coding, testing, and upgrading applications?

• ❏ A. Amazon Q Business

• ❏ B. Amazon Q Developer

• ❏ C. Amazon Q in QuickSight

• ❏ D. Amazon Q in Connect

A streaming media startup plans to use an LLM on Amazon Bedrock to evaluate customer feedback. They need the model to assign each short review a positive or negative label with reliable consistency. Which prompt design approach should they use to accomplish this?

• ❏ A. Provide the new review along with a lengthy description of how sentiment analysis and LLMs work

• ❏ B. Give several short reviews labeled positive or negative, then include the new review to be classified

• ❏ C. Submit only the new review with no context or examples

• ❏ D. Add the new review and a few examples of unrelated tasks like translation or text summarization

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A retail distributor wants to improve how autonomous inventory carts navigate around shifting obstacles in its fulfillment hubs. The team plans to train policies with reinforcement learning to increase real-time decision making and adaptability. Which statements about reinforcement learning are accurate? (Choose 2)

• ❏ A. Reinforcement Learning is primarily used to produce creative outputs such as images, audio, or long-form text

• ❏ B. Reinforcement Learning is commonly applied to robot control, autonomous driving, and complex games

• ❏ C. Reinforcement Learning is designed for static supervised tasks like classification and regression on fixed datasets

• ❏ D. Reinforcement Learning teaches an agent by interacting with an environment and learning from rewards and penalties

• ❏ E. Reinforcement Learning is the preferred approach for extracting sentiment from customer reviews and chat logs

A regional insurer is piloting a vision foundation model that classifies the severity of vehicle damage in claim photos. To confirm the model meets compliance expectations, the team needs a dependable way to measure how well the classifier performs before deployment. Which approach should they use to evaluate the model’s classification performance?

• ❏ A. Track training loss during initial epochs to predict how well the model will perform later

• ❏ B. Pick the model that ran the longest, assuming more training yields superior accuracy

• ❏ C. Use a labeled holdout dataset with ground truth and compute accuracy and F1-score

• ❏ D. Generate synthetic images to probe style shifts and compare the model’s outputs

A live sports streaming startup needs globally distributed AWS hosting that delivers very low latency and near constant availability for viewers. The site reliability engineers are evaluating how AWS is organized worldwide to keep sessions resilient across locations. Which statements accurately describe AWS Global Infrastructure? (Choose 2)

• ❏ A. Availability Zones depend on one worldwide data center

• ❏ B. An Availability Zone consists of one or more physically separate data centers

• ❏ C. Data centers within an Availability Zone are always in the same building

• ❏ D. AWS Local Zones

• ❏ E. AWS Regions generally include at least three Availability Zones

A fintech startup that offers microloans wants to apply AI to reduce fraudulent applications and streamline automated risk scoring. The analytics team is comparing classical machine learning with deep learning based on problem complexity and the amount of data they expect to process. What guidance should the team consider when distinguishing deep learning from traditional machine learning? (Choose 2)

• ❏ A. Deep learning removes the need for any data preparation, while traditional machine learning requires heavy preprocessing

• ❏ B. Deep learning is a subset of machine learning that uses multi-layer neural networks and often thrives on large datasets, while traditional machine learning typically relies on explicit feature engineering and algorithms such as decision trees or support vector machines

• ❏ C. Traditional machine learning is only for supervised learning, and deep learning is only for unsupervised learning

• ❏ D. In classical machine learning, practitioners handcraft and select features to analyze, whereas in deep learning, models can learn useful features directly from raw inputs

• ❏ E. Amazon SageMaker

A fintech company is using an LLM on Amazon Bedrock to analyze customer service conversations and recommend account actions. The workflow has several stages including intent extraction, rules-based checks, and proposing next steps. Which prompting technique will most effectively improve the model’s multi-step reasoning across these stages?

• ❏ A. Few-shot prompting

• ❏ B. Chain-of-thought prompting

• ❏ C. Retrieval-augmented generation

• ❏ D. Zero-shot prompting

Parallax Payments is deploying a fraud analytics model on AWS with Amazon SageMaker, retaining security audit logs in Amazon S3 for 45 days. The security team is updating its operational guides and needs to distinguish two disciplines. What is the primary difference between threat detection and vulnerability management in this setting?

• ❏ A. Both threat detection and vulnerability management are only about satisfying compliance audits

• ❏ B. Threat detection is about finding potential configuration weaknesses, while vulnerability management continuously watches for malicious activity

• ❏ C. Threat detection emphasizes continuous monitoring to surface active or imminent attacks, whereas vulnerability management identifies, evaluates, and remediates security weaknesses

• ❏ D. Threat detection primarily covers encryption and IAM controls, while vulnerability management focuses on incident response and service recovery

A retail analytics company is building multiple machine learning models for anomaly detection, churn prediction, and product ranking. Separate squads own each model and have distinct duties. The company wants to enforce least-privilege so members can only access the specific Amazon SageMaker resources required for their roles. Which SageMaker capability should the company use to centrally define and manage fine-grained permissions for these teams?

• ❏ A. Amazon SageMaker Model Cards

• ❏ B. AWS IAM

• ❏ C. Amazon SageMaker Role Manager

• ❏ D. Amazon SageMaker Model Monitor

A digital learning company is building a chat-based tutor and wants to improve its dialogue quality using Reinforcement Learning from Human Feedback. The team plans to use Amazon SageMaker Ground Truth to coordinate reviewers, manage labeling quality, and scale collection across 60,000 chat turns over 45 days. How can SageMaker Ground Truth assist in this workflow?

• ❏ A. Amazon Translate

• ❏ B. Generates synthetic conversation ratings so human reviewers are not needed

• ❏ C. Manages human labeling workflows and annotation UIs to capture rater feedback for RLHF

• ❏ D. Amazon CloudWatch

A clinical analytics team at Riverton Health Network is reviewing about 28 million de-identified patient encounters from the last 30 months to refine care guidelines. They are computing summary statistics, examining distributions, and creating visual dashboards to detect patterns and correlations in outcomes. These steps are intended to reveal structure and relationships before building any predictive models or advanced pipelines. Which stage of the data science lifecycle best describes this work?

• ❏ A. Data acquisition

• ❏ B. Model training

• ❏ C. Exploratory data analysis (EDA)

• ❏ D. Amazon QuickSight

A consumer electronics distributor is adopting Amazon Q Business to evaluate warehouse metrics and track procurement and delivery KPIs. The company must enforce strong access controls for product development, procurement, and leadership groups with well-defined permission boundaries. What should the organization use to manage users and assign the appropriate access in Amazon Q Business?

• ❏ A. Amazon DynamoDB

• ❏ B. AWS IAM Identity Center

• ❏ C. AWS Organizations

• ❏ D. Amazon Cognito

A research analytics company is building a summarization feature on Amazon Bedrock to condense lengthy PDFs submitted by customers. While tuning the model, the engineers are evaluating inference settings such as Response length to shape outputs. In this context, what does the Response length setting determine in Amazon Bedrock?

• ❏ A. It triggers retrieval of external documents from a knowledge source when answers seem complex

• ❏ B. It specifies the lower and/or upper limit of output tokens to constrain how long the model’s reply can be

• ❏ C. It sets stop sequences that halt generation when certain strings are produced

• ❏ D. It adjusts randomness for more or less diverse wording via parameters like temperature or top-p

A data science group at Northwind Health Labs trains models in Amazon SageMaker across two AWS Regions. For audit readiness and repeatable experiments over the next 12 months, they need a single place to record data lineage, training configuration, and evaluation results that can be shared with reviewers. Which SageMaker capability should they use to capture this information?

• ❏ A. SageMaker Feature Store

• ❏ B. SageMaker Model Cards

• ❏ C. SageMaker Clarify

• ❏ D. SageMaker Ground Truth

An international financial technology company is launching an AI-driven transaction fraud detector that ingests personal data from customers in the European Union, the United States, and the Asia-Pacific region. To build customer confidence and meet cross-border data protection obligations, which compliance framework should the company prioritize?

• ❏ A. AWS Well-Architected Framework

• ❏ B. Promotes resilient and highly available AI platforms to maintain continuity in automated decisions

• ❏ C. Emphasizes individual data privacy rights with strict consent, transparency, and accountability requirements

• ❏ D. Encourages responsible machine learning with fairness, transparency, and explainability goals

A retail startup trained a computer vision model for product recognition and exposed it through an endpoint. When this live model processes a new catalog photo to label the items it detects, what is this process called?

• ❏ A. Model deployment

• ❏ B. Performing inference

• ❏ C. Training the model

• ❏ D. Amazon Rekognition

An engineering team at Orion Vision Systems is training a vision foundation model to classify images across 180 categories for use in automated quality checks and photo search in a consumer app. Before a limited beta release, they must validate that its accuracy meets their acceptance threshold in a fair and repeatable way. What is the most appropriate approach to measure the model’s accuracy?

• ❏ A. Amazon CloudWatch

• ❏ B. Assess the model against a recognized benchmark dataset

• ❏ C. Deploy the model to production and rely on user feedback

• ❏ D. Test accuracy using a small slice of the training set

The correct choice is Multi-class assigns each example to exactly one class, while multi-label allows the same example to belong to multiple classes. In the EduStream Labs scenario the difficulty level labels are a Multi-class assigns each example to exactly one class, while multi-label allows the same example to belong to multiple classes type problem because each clip receives exactly one difficulty, and the topic tags are a Multi-class assigns each example to exactly one class, while multi-label allows the same example to belong to multiple classes type problem because a clip can have several topic tags.

In a Multi-class assigns each example to exactly one class, while multi-label allows the same example to belong to multiple classes setup the model selects one label from a fixed set for each example. In a Multi-class assigns each example to exactly one class, while multi-label allows the same example to belong to multiple classes setup each label is treated as an independent yes or no prediction so multiple labels can be active at once.

The option Multi-class does not require labeled training data, but multi-label requires labeled examples is incorrect because both multi-class and multi-label classification are supervised tasks and they both require labeled training examples to learn from.

The option Multi-class enables an item to match many categories at once, whereas multi-label restricts the item to a single category is incorrect because it reverses the definitions and would lead to wrong modeling choices.

The option Amazon Comprehend names an AWS service that can perform text classification and it can support multi-label workflows for documents, but it does not itself describe the conceptual difference asked in the question.

Remember that the key distinction is exactly one versus one or many. Map difficulty to single-label multi-class and topics to multi-label when designing your model.

Amazon SageMaker Ground Truth is the correct choice for integrating human feedback directly into the model development workflow because it provides human in the loop labeling and reviewer capabilities that feed labeled data and validation back into training and evaluation.

Amazon SageMaker Ground Truth supports private, vendor, or public workforces and it offers a managed option called Ground Truth Plus to streamline workforce operations and quality assurance. The service creates labeling pipelines, adds reviewer steps, and captures human decisions so teams can keep models accurate and trustworthy as data and risks change.

Amazon SageMaker Clarify focuses on bias detection and model explainability and it does not provide managed human labeling pipelines or workforce management so it is not the right tool for integrating human reviewers into labeling and validation workflows.

Amazon SageMaker Feature Store is a centralized repository for storing and serving features for training and inference and it is not designed to collect human annotations or run review workflows.

Amazon SageMaker Role Manager helps configure IAM roles and permissions for SageMaker users and activities and it does not facilitate human review or labeling within the ML lifecycle.

When a question mentions human labeling or reviewers think Amazon SageMaker Ground Truth because it is built to manage workforces and incorporate human feedback directly into model training and evaluation.

The correct choice is Amazon SageMaker Clarify. It is purpose built to detect and quantify bias in datasets and in model predictions and it generates explainability reports that teams can review before promoting a model to production.

Amazon SageMaker Clarify can compute fairness metrics on training data and on predicted outcomes and it can produce feature attributions to help explain which inputs drive decisions. It integrates with SageMaker training and pipeline workflows so bias and explainability checks can be automated as part of model development and pre deployment validation.

Amazon Comprehend is focused on natural language processing tasks such as entity recognition and sentiment analysis and it does not provide model fairness or bias assessment tools.

AWS Glue DataBrew helps with visual data preparation and cleaning and it lacks built in capabilities to compute model or dataset bias reports.

Amazon SageMaker Model Monitor is intended to watch deployed models for data drift and quality issues in production and it is not designed for the pre deployment bias evaluation that Clarify performs.

Use Clarify for bias and explainability checks during model build and use Model Monitor for post deployment drift and quality monitoring. When a question mentions bias or fairness before launch pick Amazon SageMaker Clarify.

Guardrails for Amazon Bedrock is the correct choice because it lets WillowWorks Edu define and enforce safety policies, word and topic filters, and contextual rules that proactively restrict model outputs so the generated stories remain age appropriate.

The guardrails capability integrates with Bedrock models to apply policy constraints at generation time. This lets the team codify age restrictions and block harmful themes before they appear in a story while still allowing creative narrative generation.

Amazon Rekognition focuses on image and video analysis with moderation labels and it does not provide enforcement controls for text produced by language models so it is not suitable for this LLM safety requirement.

Agents for Amazon Bedrock helps orchestrate tools and automate workflows but it does not itself govern or enforce content safety rules for model outputs so it does not meet the requirement.

Amazon Bedrock playgrounds are for prototyping prompts and experimenting with models and they do not add policy enforcement to guarantee age appropriate outputs so they are not the right solution.

When the scenario requires explicit enforcement of safety or policy constraints on generated text choose Guardrails for Amazon Bedrock rather than playgrounds, agents, or image services.

The correct choice is BERT-based masked language model. This approach predicts masked tokens using both left and right context so it can propose missing words in truncated log sentences and teams commonly fine tune or host such models on Amazon SageMaker for scalable inference.

BERT-based masked language model is trained with a masked language modeling objective so it learns to reconstruct words from surrounding context. Fine tuning the model on domain specific log data will improve accuracy for common log phrases and hosting the model on a managed service enables batch or real time suggestions to assist troubleshooting.

Prescriptive analytics model is focused on recommending actions or policies based on predictions and optimization and it does not generate or predict masked tokens within sentences so it cannot fill in missing words.

Amazon Comprehend provides classification and entity detection and other text analysis features but it does not perform masked token prediction and it is not designed to reconstruct truncated text.

Unsupervised clustering model groups similar log entries to surface patterns and anomalies and it can help prioritize investigations but it cannot infer exact missing words at the token level so it is not suitable for this task.

For tasks that require filling missing words choose masked language models that use bidirectional context and remember that you can fine tune and host them on Amazon SageMaker for production scale.

Amazon Transcribe and Amazon Comprehend is the correct pairing for converting call center audio into text and then determining customer sentiment from those transcripts.

Use Amazon Transcribe to convert the stored call recordings into text and then pass the transcripts to Amazon Comprehend to classify sentiment as positive negative neutral or mixed. Both services are fully managed and scale to handle tens of thousands of minutes per month so this two step flow meets the requirement for a managed speech to text and sentiment analysis pipeline.

*Amazon Transcribe* and Amazon Translate is not suitable because Amazon Translate provides language translation and not sentiment detection. The task requires sentiment analysis so Translate does not meet the requirement.

Amazon Polly and Amazon Comprehend* is not appropriate because Amazon Polly synthesizes speech from text and does not transcribe audio into text. *Amazon Comprehend handles sentiment but Polly does not provide the speech to text step needed here.

Amazon Rekognition and Amazon Transcribe* is mismatched because Amazon Rekognition analyzes images and video and does not provide NLP sentiment capability. *Amazon Transcribe can produce the transcript but Rekognition does not perform the text sentiment analysis.

Remember to run speech to text first and then apply NLP sentiment analysis on the transcripts when evaluating voice sentiment questions.

Exploratory data analysis is the correct option because the team computed summary statistics, created histograms and box plots, and generated a correlation heatmap to understand distributions and relationships before cleaning or modeling.

These steps are classic exploratory data analysis tasks and help reveal patterns, outliers, and correlations that guide subsequent decisions about cleaning and feature design. EDA provides the domain view and questions that inform how to preprocess data and which features to create.

Data preprocessing is incorrect because it mainly refers to cleaning operations such as imputing missing values, encoding categorical variables, and scaling features rather than broad profiling and visualization.

Feature engineering is incorrect because it focuses on creating or transforming input variables to improve model performance and not on initial visual exploration and summary statistics.

Hyperparameter tuning is incorrect because it takes place after a model and features are in place and involves searching for optimal model settings like learning rate or tree depth.

Look for words like summary statistics or visualizations to spot exploratory data analysis and look for imputation or scaling to identify preprocessing.

Amazon SageMaker and Amazon Comprehend Medical are correct because they provide the managed ML tooling and the domain aware text extraction needed to build predictive healthcare analytics from clinical notes.

Amazon SageMaker supplies an end to end managed environment to prepare data train models tune performance and deploy inference endpoints so teams can build and operationalize patient risk scoring pipelines at scale.

Amazon Comprehend Medical extracts clinical entities such as conditions medications and test results from unstructured notes and it produces structured outputs that can be transformed into features for downstream predictive models which speeds feature engineering for healthcare use cases.

Amazon Kendra is incorrect because it is a search and question answering service for finding and surfacing documents and it does not perform predictive modeling or produce model features by itself.

Amazon Polly is incorrect because it converts text into lifelike speech for voice applications and it provides no analytics or prediction capabilities for clinical outcomes.

Amazon Rekognition is incorrect because it offers general image and video analysis capabilities such as object detection and facial analysis and it is not designed to determine clinical diagnoses or directly produce patient risk predictions.

Use Comprehend Medical to extract structured features from notes and use SageMaker to prototype validate and deploy models and always confirm de identification and access controls when working with healthcare data.

The best choices are They make debugging and performance tuning more straightforward and They increase stakeholder trust by making decisions understandable.

Transparent models make it easier to trace how input features affect outputs so engineers can identify errors, measure feature importance, and focus tuning efforts efficiently. This clarity supports reproducible decision logs and speeds remediation when auditors or compliance teams request explanations.

Explainable models also let reviewers and stakeholders inspect the reasoning behind flagged transactions which builds confidence and accountability. Clear explanations reduce back-and-forth with auditors and help the team demonstrate compliance during regular reviews.

They lower infrastructure and storage requirements is incorrect because resource needs are determined by model complexity and data volume rather than whether a model is explainable.

AWS Key Management Service is incorrect because it is an encryption key management service and not a property or benefit of choosing an interpretable model.

They strengthen security by obscuring decision rules is incorrect because obscuring rules works against auditability and trust and it is the opposite of why teams choose explainable models.

When a question emphasizes auditability or regulatory review favor models that provide clear explanations and traceable decisions because they simplify debugging and increase stakeholder confidence.

Amazon Q in Connect is the correct choice because it integrates natively with Amazon Connect to deliver real time agent assistance such as context aware suggested replies, next best actions during calls, and automated after call summaries that help reduce average handle time.

Because it is embedded in the contact center, Amazon Q in Connect can access call context, transcripts, and customer history to generate context aware replies and recommend next best actions in real time. It can also produce automated after call summaries and task suggestions which reduce manual wrap up work and improve agent productivity. These capabilities align directly with the stated goal to lower average handle time while supporting human agents.

Amazon Q Business focuses on enterprise knowledge retrieval and content generation across business applications and it is not primarily designed to be embedded in live contact center interactions to provide real time agent assist.

Amazon Q in QuickSight centers on analytics and generative business intelligence and it does not provide native agent guidance or in call integration for Amazon Connect.

Amazon Q Developer targets software development and IT operations use cases and it is not aimed at delivering customer service agent assist features during live calls.

When a scenario specifies live, in call agent guidance choose the option embedded with Amazon Connect and look for keywords such as real time agent assist and contact center integration.

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The correct action is Enable invocation logging in Amazon Bedrock. This setting captures both model input and output payloads so the team can store requests and responses for audit and troubleshooting without building custom pipelines.

Enable invocation logging in Amazon Bedrock is a native Bedrock capability that records invocation payloads and writes them to configured destinations so you get full request and response context for debugging and compliance. Enabling this feature meets the requirement directly because Bedrock produces the logs and preserves the model I O data.

Configure AWS CloudTrail as the logs destination for the model is incorrect because CloudTrail focuses on control plane API activity and management events and it does not capture full model request or response bodies.

Configure model invocation logging in Amazon EventBridge is incorrect because EventBridge is an event routing service and not a feature that itself captures or stores detailed invocation payloads.

Send model logs directly to Amazon CloudWatch Logs is insufficient on its own because CloudWatch Logs is a destination and does not create invocation payload logs unless invocation logging is enabled in the Bedrock service.

Look inside the Bedrock settings when a question asks to capture model input and output payloads. Choose the built in invocation logging option rather than external audit or routing services.

Amazon Bedrock Knowledge Bases is the correct option because it provides a built in retrieval augmented generation workflow that ingests documents from S3, creates embeddings, and retrieves relevant content at query time so the Bedrock foundation model can answer FAQs using private manuals and policies without custom training.

Knowledge Bases handles document ingestion, chunking, and semantic retrieval so you do not need to build a separate pipeline or fine tune a model. This approach keeps the foundation model unchanged and lets the assistant ground responses with up to date proprietary content stored in S3.

Amazon Kendra is a capable enterprise search solution but it requires additional integration steps to feed results into Bedrock for RAG and it is not the most direct in‑platform path for grounding a Bedrock model.

Amazon Bedrock Guardrails focuses on safety, policy enforcement, and response constraints and it does not perform document retrieval or inject private knowledge into model answers.

Amazon SageMaker training would require fine tuning or training a model which is more costly and unnecessary when RAG with a Bedrock Knowledge Base can provide grounded answers without retraining.

When a question asks for the simplest way to ground a foundation model with private documents pick a RAG solution such as Bedrock Knowledge Bases instead of planning to retrain or fine tune a model.

The most effective combination is Retrieval-augmented generation (RAG) and Prompt engineering techniques. Retrieval-augmented generation (RAG) anchors answers in your Bedrock FAQ knowledge base and Prompt engineering techniques shape responses to be concise and predictable which helps meet the target of under 300 milliseconds.

With Retrieval-augmented generation (RAG) the system retrieves relevant FAQ passages and conditions the model on those passages so answers are factual and traceable. Proper indexing, relevance tuning and caching of retrieved passages reduce lookup time and lower end to end latency.

Using Prompt engineering techniques you design templates, constraints and output formats that keep answers short and consistent. Effective prompts steer the model toward brief outputs, make results easier to validate and improve predictability of response time.

BLEU metric tuning focuses on evaluating machine translation quality and it does not improve factual grounding or concise answer formatting for an FAQ assistant.

Multilingual model support is useful when you must serve multiple languages but it does not inherently increase accuracy or brevity for an English FAQ use case.

Model fine-tuning can adapt style and domain knowledge but it is heavier, slower and costlier to iterate than combining retrieval and prompt work. Fine tuning also does not automatically keep answers grounded to the latest FAQ content unless you continuously retrain on updated sources.

Combine retrieval grounding with concise prompt templates and cache retrieved snippets to help keep responses under 300 milliseconds.

The correct choice is Amazon SageMaker Studio because it provides a unified, fully managed workspace where teams can build, train, tune, and deploy models without provisioning or maintaining the underlying infrastructure.

Studio is an integrated development environment that combines notebooks, experiment management, model building, training, debugging, and deployment into a single web based interface. It handles provisioning and scaling of compute and storage behind the scenes so the research team can focus on model development instead of server setup and infrastructure management.

Amazon SageMaker Ground Truth is focused on data labeling and creating high quality training datasets and it does not offer the end to end IDE for building training and deployment in one managed workspace.

Amazon SageMaker Autopilot automates model building and tuning through AutoML but it is not the comprehensive interactive development and deployment environment that Studio provides.

Amazon SageMaker JumpStart offers pretrained models and solution templates to accelerate starts yet it serves as an accelerator rather than the central managed IDE for the full model lifecycle.

When a question emphasizes no infrastructure management and build, train, and deploy in one place choose Amazon SageMaker Studio. Use associations like AutoML for Autopilot and data labeling for Ground Truth to eliminate distractors.

The correct choice is It modulates sampling randomness, making outputs more predictable at low values and more varied at higher values. It modulates sampling randomness, making outputs more predictable at low values and more varied at higher values rescales the model’s logits so that lower settings favor high probability tokens and higher settings allow more exploratory, creative token selection.

It modulates sampling randomness, making outputs more predictable at low values and more varied at higher values is correct because the temperature value divides the logits before the softmax step and that changes the relative probabilities of next tokens. Lower temperature concentrates probability mass on the top tokens and yields more predictable text while higher temperature flattens the distribution and increases the chance of sampling less likely tokens.

It enforces a hard cap on the number of tokens returned to keep responses short and uniform is incorrect because length is governed by parameters such as max_tokens rather than by temperature, and temperature changes randomness not output length.

It sets a nucleus sampling threshold top p that restricts choices to a cumulative probability cutoff is incorrect because that description matches the top_p nucleus sampling parameter which limits the candidate pool by cumulative probability and is a separate control from temperature.

It removes all low-probability tokens so the model always chooses the single most likely next token is incorrect because that behavior matches greedy decoding and temperature does not remove tokens but only adjusts their sampling probabilities.

Remember that temperature controls randomness while max_tokens controls length and top_p controls the cumulative probability pool. When you see deterministic versus diverse outputs think temperature.

The correct option is Temperature. This parameter controls the randomness of the model so lower values produce predictable repeatable text and higher values produce more varied creative responses when invoking models through Amazon Bedrock.

Temperature works by scaling the model logits before sampling and so it serves as a direct creativity dial for identical prompts. For deterministic marketing copy you can choose a low temperature such as 0 or 0.2. For more imaginative or surprising copy you can raise the temperature toward 0.8 or 1.0 and then iterate until the tone matches your needs.

Top K limits the next token choices to a fixed number of the most likely candidates and this can affect diversity but it is not the primary control for making outputs more or less creative compared with Temperature.

Top P sets a cumulative probability cutoff to form a candidate set for sampling and it shapes the distribution of possible tokens but it does not provide the straightforward creativity adjustment that Temperature does.

Stop sequences only tell the model where to stop generating text and they do not influence how imaginative or deterministic the content itself will be.

When a question asks about making outputs more creative or more deterministic think Temperature first. Save Top K and Top P for finer control of token selection when needed.

Balance and de-bias the training corpus to reduce harmful skew and Add fairness metrics to the model evaluation process are correct because responsible AI for policy review must address data harms and measure outcomes to confirm fairness. Teams can use tools such as Amazon SageMaker Clarify to detect and monitor bias during data preparation and model evaluation.

Balance and de-bias the training corpus to reduce harmful skew reduces systematic errors that could disadvantage particular groups of policyholders and it improves the validity of downstream fairness checks. Add fairness metrics to the model evaluation process creates measurable signals that governance teams can audit and that guide mitigation efforts before deployment.

Tune the temperature parameter to 0.2 for more consistent responses is about generation variability and it does not change biased patterns in the training data nor provide accountability for fairness.

Use prompt engineering to coax the model toward desired formats can help shape outputs but it is not a substitute for fixing biased data or for measuring fairness during evaluation.

Train longer to memorize domain terminology and reduce loss risks overfitting and it does not inherently address fairness or ethical risks so it is not a responsible mitigation strategy on its own.

When a question highlights responsible or ethical AI prefer answers about data de-biasing and fairness evaluation rather than hyperparameter tweaks or prompt tricks.

The correct option is AWS PrivateLink. It enables private connectivity from a VPC to supported AWS services and third party services by using interface VPC endpoints so traffic does not traverse the public internet.

AWS PrivateLink provisions interface endpoints inside the VPC that expose the service on private IP addresses. This design keeps service-to-service calls on the AWS network rather than the public internet and it works with VPC routing and security groups to provide network level control over traffic.

IAM policies control which identities can call APIs and what actions they can perform but they do not create private network paths or stop traffic from traversing the public internet.

AWS Macie focuses on discovering and protecting sensitive data in Amazon S3 and it does not provide private endpoints or service connectivity.

AWS Config records resource configurations and evaluates compliance so it is an auditing and inventory tool and it does not deliver private service-to-service connectivity between a VPC and AWS service endpoints.

When a question stresses keeping traffic off the public internet think AWS PrivateLink and interface VPC endpoints rather than access control or auditing services.

The correct choice is Amazon Q Developer because it is designed for software engineers and integrates with IDEs to assist with code generation, unit test creation, refactoring, security scanning, and automated code upgrades, matching the team need to code, test, and modernize applications.

Amazon Q Developer automates repetitive developer tasks and helps accelerate coding and migration work by suggesting fixes, creating tests, and recommending modernization steps that can be applied across languages and frameworks.

Amazon Q Business focuses on enterprise knowledge tasks such as answering questions over internal content and automating business workflows and it does not provide developer IDE integrations or tooling for code upgrades.

Amazon Q in QuickSight delivers a generative business intelligence experience for analytics and dashboards and it is not intended for writing code, running unit tests, or performing application migrations.

Amazon Q in Connect supports contact center agents with real time recommendations and customer interactions and it does not address software development workflows like coding or test automation.

When a scenario highlights IDE integration, unit tests, or code modernization choose the developer focused variant because it targets programming workflows rather than business analytics or contact center use cases.

The correct option is Give several short reviews labeled positive or negative, then include the new review to be classified. This few shot prompt gives the model concrete examples of the mapping you want and an expected output format so it can apply the same labeling to new reviews.

Using a few shot approach helps the model learn the classification rule from examples and reduces variance in outputs. You should include clear labeled examples and request a constrained response such as exactly the words positive or negative so the model returns a reliable binary label.

Provide the new review along with a lengthy description of how sentiment analysis and LLMs work is not effective because theoretical descriptions do not demonstrate the exact output format and they do not constrain the model to produce only the required labels.

Submit only the new review with no context or examples is a zero shot prompt and it often yields inconsistent or verbose replies instead of the precise positive or negative tag you need.

Add the new review and a few examples of unrelated tasks like translation or text summarization mixes objectives and can confuse the model so it may not focus on sentiment classification and accuracy can drop.

Use few shot labeled examples and explicitly ask for a constrained output such as exactly positive or negative to improve consistency on Bedrock.

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The correct statements are Reinforcement Learning is commonly applied to robot control, autonomous driving, and complex games and Reinforcement Learning teaches an agent by interacting with an environment and learning from rewards and penalties.

Reinforcement Learning teaches an agent by interacting with an environment and learning from rewards and penalties describes the trial and error loop that produces policies which improve with experience. Reinforcement Learning is commonly applied to robot control, autonomous driving, and complex games fits because these domains require sequential decision making, online adaptation, and control policies that maximize long term return under changing conditions.

Reinforcement Learning is primarily used to produce creative outputs such as images, audio, or long-form text is incorrect because generative models such as diffusion models and large language models are the typical tools for content creation and reinforcement learning is only occasionally used to fine tune behavior.

Reinforcement Learning is designed for static supervised tasks like classification and regression on fixed datasets is incorrect since those tasks belong to supervised learning where models learn from labeled examples without interacting with an environment.

Reinforcement Learning is the preferred approach for extracting sentiment from customer reviews and chat logs is incorrect because sentiment analysis is usually solved with natural language processing methods trained with supervised or transfer learning rather than with reinforcement learning.

When you see the words agent, environment, and reward think reinforcement learning and when you see labeled data or classification think supervised learning.

Use a labeled holdout dataset with ground truth and compute accuracy and F1-score is correct because it gives an objective and repeatable assessment of how the classifier performs on unseen claim photos and it reports both overall correctness and the balance between precision and recall.

Evaluating on a labeled holdout dataset measures generalization and prevents optimistic bias from training data. Accuracy summarizes overall agreement and F1-score combines precision and recall so it is especially useful when damage severity classes are imbalanced or when false positives and false negatives have different business costs.

Track training loss during initial epochs to predict how well the model will perform later is inadequate because training loss reflects fit to the training data and does not indicate real world generalization to new images.

Pick the model that ran the longest, assuming more training yields superior accuracy is flawed because wall clock training time is not a proxy for model quality and longer runs can simply reflect inefficient hyperparameters or overfitting.

Generate synthetic images to probe style shifts and compare the model’s outputs can help with robustness testing and uncover distribution shifts but synthetic data should not replace a properly labeled holdout set for formal accuracy measurement.

Use a ground-truth labeled test set and prefer F1 when classes are imbalanced. Inspect a confusion matrix to find common misclassifications.

The correct statements are An Availability Zone consists of one or more physically separate data centers and AWS Regions generally include at least three Availability Zones.

An Availability Zone consists of one or more physically separate data centers are built from multiple discrete facilities so they avoid single points of failure and they provide low latency network connectivity for applications inside the same region. AWS Regions generally include at least three Availability Zones are designed with multiple AZs so architects can place resources across AZs for high availability and fault isolation and many regions include three or more AZs to support resilient architectures.

Availability Zones depend on one worldwide data center is incorrect because Availability Zones are local to a Region and they are not a single global facility.

Data centers within an Availability Zone are always in the same building is incorrect because AZs comprise physically separate data centers and those facilities are typically in different buildings to reduce correlated failures.

AWS Local Zones is not an answer to how Regions and Availability Zones are organized because Local Zones extend a Region by placing compute and storage closer to users and they do not change the fundamental Region and AZ structure.

On the exam remember that Availability Zones map to one or more discrete data centers and that Regions group multiple AZs so design solutions to span AZs for fault tolerance.

Deep learning is a subset of machine learning that uses multi-layer neural networks and often thrives on large datasets, while traditional machine learning typically relies on explicit feature engineering and algorithms such as decision trees or support vector machines and In classical machine learning, practitioners handcraft and select features to analyze, whereas in deep learning, models can learn useful features directly from raw inputs are the correct guidance to distinguish the two approaches.

The first statement captures the methodological difference because deep learning uses multiple neural network layers that can learn hierarchical representations and they generally perform best when large volumes of labeled or unlabeled data are available. The second statement highlights how feature creation differs across approaches because traditional algorithms often need domain experts to engineer and select features while deep models can discover useful features from raw text, images, or high dimensional signals.

Deep learning removes the need for any data preparation, while traditional machine learning requires heavy preprocessing is incorrect because both deep and classical methods still require data cleaning, normalization, correct labeling, and attention to data quality and class balance.

Traditional machine learning is only for supervised learning, and deep learning is only for unsupervised learning is wrong because both families of techniques can be applied to supervised, unsupervised, and reinforcement learning tasks depending on the algorithm and problem setup.

Amazon SageMaker is not a conceptual distinction between the paradigms and it is incorrect as an answer because it is an AWS service that can host, train, and deploy both traditional machine learning and deep learning models rather than representing one approach or the other.

Favor deep learning when you have large or unstructured data and you need automatic feature learning. Otherwise prefer traditional methods for faster training and easier interpretation.

The correct option is Chain-of-thought prompting. This approach is most effective for improving multi step reasoning across stages like intent extraction, rules based checks, and proposing account actions.

Chain-of-thought prompting prompts the model to break a complex decision into intermediate steps and to justify transitions between those steps. That explicit, step by step output increases traceability and helps the model align its recommendations with extracted intent and policy checks, which makes it easier to validate each stage of the workflow.

Few-shot prompting can provide examples that improve output format and style, but it does not force the model to generate explicit intermediate reasoning so it is less reliable for procedural multi stage tasks.

Zero-shot prompting gives no examples or scaffolding and it rarely yields consistent multi step chains of reasoning for complex workflows without further structure.

Retrieval-augmented generation supplies external context and factual grounding and it is useful for adding relevant information, but it does not by itself enforce sequential, step by step reasoning across the pipeline.

When a question highlights multi step or procedural reasoning choose Chain-of-thought prompting and ask the model to list intermediate steps and brief justifications to improve traceability.

The correct answer is Threat detection emphasizes continuous monitoring to surface active or imminent attacks, whereas vulnerability management identifies, evaluates, and remediates security weaknesses. In AWS this typically maps to continuous detectors for active threats and scanning or assessment tools for vulnerabilities.

Threat detection focuses on continuous collection and analysis of telemetry so teams can surface suspicious activity quickly and respond. Services such as Amazon GuardDuty examine VPC flow logs, AWS CloudTrail, and DNS logs to detect indicators of compromise and generate alerts for investigation.

Vulnerability management is a proactive lifecycle that inventories assets, scans for software and configuration flaws, prioritizes findings by risk, and tracks remediation or patching. Tools like Amazon Inspector help discover and prioritize vulnerabilities so teams can fix or mitigate them before they are exploited.

Both threat detection and vulnerability management are only about satisfying compliance audits is incorrect because these disciplines primarily exist to reduce risk and to detect and remediate security issues, and they only secondarily support compliance.

Threat detection is about finding potential configuration weaknesses, while vulnerability management continuously watches for malicious activity is incorrect because this statement inverts the responsibilities. Monitoring for active or imminent attacks belongs to threat detection and identifying weaknesses belongs to vulnerability management.

Threat detection primarily covers encryption and IAM controls, while vulnerability management focuses on incident response and service recovery is incorrect because encryption and IAM are preventive controls that span many security areas and incident response and recovery are broader operational functions, and those distinctions do not define the two disciplines.

Map action words to each discipline by thinking monitor, detect, alert for threat detection and identify, assess, remediate, patch for vulnerability management.

The correct choice is Amazon SageMaker Role Manager. This service lets teams centrally define and manage fine-grained, SageMaker-specific permissions so each squad can have least-privilege access to only the resources they need.

Amazon SageMaker Role Manager is designed to create and assign scoped, role-based permissions for SageMaker resources such as notebooks, training jobs, endpoints, and pipelines. Role Manager simplifies mapping team responsibilities to roles that match SageMaker job functions and reduces the effort of writing many custom policies while still enforcing least-privilege.

Amazon SageMaker Model Cards focuses on model documentation and transparency and does not provide mechanisms to assign or enforce access controls for teams.

AWS IAM is the underlying identity and access management service and is required for policies and roles. It is not the SageMaker-native management tool that streamlines creating and assigning scoped SageMaker roles so Role Manager is the better exam answer.

Amazon SageMaker Model Monitor is used to track data quality and model drift and it does not manage user permissions or role assignment.

When a question emphasizes SageMaker-native, fine-grained permission management and least-privilege choose the SageMaker governance feature rather than general IAM or monitoring tools.

The correct choice is Manages human labeling workflows and annotation UIs to capture rater feedback for RLHF.

Amazon SageMaker Ground Truth provides managed labeling jobs and workforce orchestration along with customizable annotation user interfaces and built in quality controls that let teams collect high quality human preference labels at scale for RLHF training. Ground Truth integrates with SageMaker data storage and pipelines so collected ratings and reviewer metadata can be exported for reward model training and iterative policy updates.

Amazon Translate is designed for language translation and does not provide workforce management or structured labeling UIs so it cannot coordinate reviewers or capture RLHF preference labels.

Generates synthetic conversation ratings so human reviewers are not needed is incorrect because RLHF depends on real human judgments to align model behavior with human preferences and synthetic ratings cannot replace human in the loop annotation for alignment tasks.

Amazon CloudWatch focuses on monitoring logs and metrics for observability and does not manage human labeling workflows or annotation tasks so it does not address RLHF data collection needs.

Choose services that support human-in-the-loop workflows, custom annotation UIs, and quality control when you need RLHF labels because those capabilities ensure scalable and reliable rater data.

Exploratory data analysis (EDA) is the correct stage for the work described because the team is computing summary statistics, examining distributions, and creating visual dashboards to reveal structure and relationships before building predictive models.

EDA emphasizes understanding the data through statistical summaries and visualizations and it helps uncover patterns, anomalies, and correlations that inform feature engineering and modeling choices. In this scenario the analysts are inspecting 28 million de-identified encounters and producing dashboards to detect patterns prior to any modeling which matches EDA activities.

Model training is incorrect because it refers to fitting algorithms to prepared features and tuning model parameters which occurs after data exploration and preparation, not during the initial summary and visualization work.

Data acquisition is incorrect because it focuses on collecting or ingesting raw data and does not include the statistical summaries or visualization-driven pattern discovery described here.

Amazon QuickSight is incorrect because it is a specific visualization service that can support EDA tasks, but it is not a stage in the data science lifecycle and the question asks for the stage rather than a particular tool.

Watch for phrases like summary statistics, distributions, and before modeling to quickly identify EDA on exam questions.

The correct option is AWS IAM Identity Center which is the appropriate service to manage users and assign permission boundaries for Amazon Q Business.

AWS IAM Identity Center centralizes workforce identity and group management and it integrates with external identity providers and enterprise directories. It lets administrators create permission sets and assign them to groups so users receive consistent least privilege access across AWS accounts and services including Amazon Q Business.

Amazon DynamoDB is a managed NoSQL database and it does not provide authentication or authorization workflows for workforce access or permission sets.

AWS Organizations is used to manage multiple AWS accounts and to apply account level policies and it is not intended for user sign in flows or per user application permission assignment.

Amazon Cognito focuses on customer identity for web and mobile applications and it is not the best fit for centralized workforce single sign on and permission sets to AWS services.

For workforce access to AWS services choose AWS IAM Identity Center and use Amazon Cognito when the requirement is customer facing authentication.

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The It specifies the lower and/or upper limit of output tokens to constrain how long the model’s reply can be option is correct. This setting defines how long the generated summary can be in terms of tokens and it directly controls the length of the model output.

In Amazon Bedrock the It specifies the lower and/or upper limit of output tokens to constrain how long the model’s reply can be behavior is realized through invocation parameters such as max_tokens and sometimes min_tokens which cap or guide how many tokens the model may produce. Adjusting these values helps produce concise summaries or longer explanations depending on the requirement.

It triggers retrieval of external documents from a knowledge source when answers seem complex is incorrect because that option describes retrieval augmented generation or agent functionality which fetches external context and does not set how many tokens the model will output.

It sets stop sequences that halt generation when certain strings are produced is incorrect because stop sequences only define termination patterns and they do not impose an overall token budget for the response.

It adjusts randomness for more or less diverse wording via parameters like temperature or top-p is incorrect because those parameters control sampling behavior and creativity rather than enforcing a length limit on the generated text.

When a question asks about controlling summary length look for wording about upper or lower bounds or tokens. Remember that temperature and top-p change randomness and that stop sequences only define termination points.

The correct choice is SageMaker Model Cards because they provide a centralized and standardized place to record dataset lineage training configuration and evaluation results across regions to support audit readiness and reproducible experiments.

SageMaker Model Cards capture metadata such as dataset provenance model configuration hyperparameters and evaluation metrics and they can be packaged as artifacts that reviewers can inspect to verify experiment inputs and outcomes. This makes them suitable as a single shared record for reviewers and auditors over the next 12 months.

SageMaker Feature Store is designed to store and serve features for training and inference and it does not provide the governance documentation or structured model documentation needed for audit trails.

SageMaker Clarify helps detect bias and generate explanations for model predictions and it is valuable for responsible AI, but it does not serve as a comprehensive documentation artifact for lineage configuration and evaluation records.

SageMaker Ground Truth manages data labeling jobs and the creation of labeled datasets and it is not intended to document model parameters performance outcomes or audit-ready experiment records.

When the exam asks about documentation transparency and auditability think Model Cards first and then map other SageMaker services to their primary roles.

Emphasizes individual data privacy rights with strict consent, transparency, and accountability requirements is correct because it reflects the General Data Protection Regulation which applies to personal data of EU residents and sets binding rules for lawful processing, consent where required, transparency, data subject rights, and accountability that the company must follow when it handles EU customer data.

Emphasizes individual data privacy rights with strict consent, transparency, and accountability requirements maps directly to GDPR obligations and therefore is the framework the company should prioritise for cross border protections. GDPR has extraterritorial reach and requires measures such as data protection impact assessments for high risk processing like automated fraud detection, strong legal bases for processing, minimisation and purpose limitation, and safeguards for international transfers such as adequacy decisions or standard contractual clauses. Prioritising these requirements helps build customer confidence and meets statutory compliance obligations across the regions involved.

AWS Well-Architected Framework is not a legal or regulatory framework. It provides cloud architecture best practices for security, reliability, performance and cost but it does not satisfy statutory privacy obligations or replace laws such as GDPR.

Promotes resilient and highly available AI platforms to maintain continuity in automated decisions focuses on availability and resilience of systems. That emphasis does not address consent, data subject rights, or cross border transfer controls so it does not meet the compliance needs described.

Encourages responsible machine learning with fairness, transparency, and explainability goals covers ethical AI principles and governance and it can support compliance efforts. It does not however substitute for binding data protection laws that impose legal obligations for consent, lawful basis, record keeping, and transfer safeguards.

When EU personal data is involved think GDPR first and separate legal privacy obligations from architecture guidance and ethical AI frameworks.

The correct option is Performing inference. This is the process used when a trained model at a live endpoint analyzes a new catalog photo and returns labels or detections.

Performing inference means applying a trained model to new inputs so it produces predictions such as classifications or bounding boxes. The startup calls its deployed model with the photo and the model performs inference to identify the products in the image.

Model deployment is about hosting and exposing the model so it can be called. Deployment happens before predictions are served and it does not describe the act of producing labels.

Training the model is the stage where the model learns parameters from labeled data through repeated updates. Training is not the step where the trained model is used to predict new samples.

Amazon Rekognition is a managed computer vision service provided by AWS and it is not the name of the process. The question asks for the name of the action performed by the deployed custom model which is inference.

When a question mentions new inputs producing predictions look for the term inference or verbs like predict and classify since those indicate the model is making predictions rather than training or being hosted.

Assess the model against a recognized benchmark dataset is the correct option because it gives an objective, fair, and repeatable measure of accuracy that was not used during training.

Evaluating on a standardized held out benchmark ensures results can be compared across models and research efforts and it reduces the risk of data leakage. A benchmark that covers or closely matches the 180 categories gives a reliable view of generalization and supports statistical testing and detailed error analysis.

Amazon CloudWatch is focused on operational telemetry and infrastructure metrics and it does not provide mechanisms for held out dataset evaluation or computing model classification accuracy.

Deploy the model to production and rely on user feedback postpones proper validation until after release and it risks harming users while producing noisy and biased signals that are not a reproducible accuracy measurement.

Test accuracy using a small slice of the training set creates optimistic estimates because of data leakage and overfitting and it does not reflect how well the model will perform on new images.

Use a held out validation or benchmark dataset that was not used for training and make sure the dataset represents the 180 categories and the expected production distribution.