AWS Certified Machine Learning Specialist Exam Topics & Practice Exams

Exam basics

This exam measures your ability to apply machine learning knowledge in the AWS environment. It contains multiple choice and multiple response questions. Your result appears as a scaled score between 100 and 1000. The minimum passing score is 750 which is consistent with the expectations for advanced level certifications. The scoring model is compensatory which means you pass based on your total performance rather than on each section. The exam also includes unscored questions that are used by AWS to test potential content for future versions.

Content domains and weights

The content is divided into four domains that each focus on specific knowledge areas. Data engineering makes up 20 percent of the exam. Exploratory data analysis accounts for 24 percent. Modeling has the largest share at 36 percent. Machine learning implementation and operations represent the remaining 20 percent. Some concepts overlap with the AWS AI Practitioner exam (and the GCP Generative AI Leader certification) but this exam requires deeper technical expertise.

Domain 1: data engineering

This domain tests your knowledge of creating and managing data pipelines that support machine learning workloads.

Creating data repositories

You need to know how to identify sources of data and how to store them using services such as Amazon S3, Amazon EFS, and Amazon EBS.

Data ingestion pipelines

You should understand how to design both batch and streaming pipelines using services such as Amazon Kinesis, Amazon Data Firehose, AWS Glue, Amazon EMR, and Amazon Managed Service for Apache Flink.

Data transformation

You should know how to process and transform data using ETL tools such as AWS Glue, AWS Batch, and Amazon EMR, as well as MapReduce frameworks like Apache Spark and Apache Hive.

Domain 2: exploratory data analysis

This domain measures your ability to prepare and understand datasets before building models.

Data sanitization and preparation

You need to identify and correct missing data, normalize and scale datasets, and ensure there is enough labeled data for training.

Feature engineering

You should be able to extract features from text, speech, images, and public datasets, and apply techniques such as tokenization, binning, one hot encoding, and dimensionality reduction.

Visualization and statistics

You should create and interpret visualizations such as scatter plots, histograms, and cluster analysis. You must also understand statistical measures including correlation, summary statistics, and p values.

Domain 3: modeling

This domain evaluates your knowledge of choosing and training the right machine learning models.

Framing ML problems

You should know how to decide when machine learning is the right solution and how to differentiate between supervised and unsupervised learning. You should also map business needs to regression, classification, clustering, forecasting, recommendation, or foundation models.

Model selection

You should know the capabilities of algorithms such as XGBoost, linear and logistic regression, k means, random forests, convolutional and recurrent neural networks, ensemble methods, transfer learning, and large language models.

Training

You need to understand how to split data for cross validation, how gradient descent works, how to ensure convergence, and when to use GPU, CPU, or distributed platforms such as Spark.

Hyperparameter tuning

You should understand methods such as dropout, L1 and L2 regularization, learning rate adjustments, and initialization for neural networks, tree models, and linear models.

Evaluation

You must be able to interpret metrics including accuracy, precision, recall, F1 score, RMSE, ROC curves, and AUC. You should also evaluate confusion matrices, compare models based on efficiency and cost, and perform A B testing.

Domain 4: machine learning implementation and operations

This domain focuses on deploying and maintaining ML solutions in production environments.

Building reliable ML solutions

You should know how to deploy across multiple Availability Zones and Regions, use Auto Scaling and load balancing, monitor with CloudTrail and CloudWatch, and package models with Docker containers.

Choosing AWS services

You should understand when to use services such as Amazon Polly, Amazon Lex, Amazon Transcribe, and Amazon Q, and when to build custom models with Amazon SageMaker.

Security practices

You should apply IAM policies, S3 bucket configurations, VPC isolation, and encryption techniques to protect ML solutions.

Deployment and monitoring

You should know how to expose ML endpoints, conduct A B testing, build retraining pipelines, debug errors, and monitor performance over time.

Out of scope tasks

You are not expected to develop complex algorithms from scratch, perform advanced mathematical proofs, or manage low level network design. You do not need to conduct deep DevOps tasks for EMR or manage unrelated platforms such as the Google Cloud Platform. The exam stays focused on AWS machine learning services and practical implementation.

How to prepare

Start with the official certification exam topics and make sure you understand the requirements for each domain. Use practice exams to gain familiarity with the format and to highlight areas that need more study.

You can use generative AI as a study partner by asking for explanations of topics and requesting practical walkthroughs. Return to the mock exams for another round of review to close any knowledge gaps before taking the real exam.

Cameron McKenzie is an AWS Certified AI Practitioner, Machine Learning Engineer, Solutions Architect and author of many popular books in the software development and Cloud Computing space. His growing YouTube channel training devs in Java, Spring, AI and ML has well over 30,000 subscribers.

Next Steps

The AWS Solutions Architect Book of Exam Questions by Cameron McKenzie

So what’s next?

A great way to secure your employment or even open the door to new opportunities is to get certified. If you’re interested in AWS products, here are a few great resources to help you get Cloud Practitioner, Solution Architect, Machine Learning and DevOps certified from AWS:

• AWS Certified Cloud Practitioner Certification
• AWS Certified Developer Associate Certification
• AWS Certified AI Practitioner Certification & Answers
• AWS Certified Machine Learning Associate Certification
• AWS Certified DevOps Professional Certification
• AWS Certified Data Engineer Associate Certification
• AWS Certified Solutions Architect Associate Certification

Put your career on overdrive and get AWS certified today!