AWS Certified Machine Learning - Specialty Syllabus 2026 — Full Topic-by-Topic Breakdown

The AWS Certified Machine Learning - Specialty (MLS-C01) exam is not just another multiple-choice test. It’s a hands-on, scenario-driven assessment that expects you to think like a machine learning engineer building on AWS. The official syllabus is split into four domains, each with a fixed weightage. Here’s what’s actually covered, how much it matters, and how to study each section without wasting time.

Domain 1: Data Engineering (20%)

This domain tests your ability to prepare and orchestrate data for ML pipelines. Expect questions on ingesting streaming and batch data, transforming it, and storing it in the right AWS service.

Key topics:

• Kinesis (Data Streams, Data Firehose, Video Streams)
• S3, Glue, and Athena for data cataloging and querying
• EMR and Spark for large-scale processing
• Data formats (Parquet, Avro, JSON) and compression
• Streaming vs. batch trade-offs

Study tip: Focus on when to use Kinesis vs. SQS vs. SNS. Know how Glue DataBrew and Glue ETL fit into a pipeline. Practice building a simple streaming pipeline on AWS, even if it’s just a demo.

Domain 2: Exploratory Data Analysis (24%)

This is where you prove you can inspect data, detect issues, and decide whether it’s ready for modeling. It’s the second-largest domain, so don’t skip it.

Key topics:

• Data visualization with QuickSight, Athena, and SageMaker Studio
• Handling missing values, outliers, and imbalanced data
• Feature engineering (scaling, encoding, binning)
• Bias detection and data leakage
• Using SageMaker Data Wrangler and AWS Glue Interactive Sessions

Study tip: Practice with real datasets. Know the difference between imputation strategies and when to drop rows. Understand how to use SMOTE or class weights for imbalance. Be comfortable reading confusion matrices and ROC curves.

Domain 3: Modeling (36%)

This is the core of the exam. It covers the entire model lifecycle: choosing the right algorithm, training, tuning, and deploying. It’s the heaviest domain, so allocate the most study time here.

Key topics:

• SageMaker built-in algorithms (XGBoost, Linear Learner, K-Means, etc.)
• Hyperparameter tuning (Automatic Model Tuning, early stopping)
• Training on GPU vs. CPU, distributed training
• Model deployment: real-time endpoints, serverless inference, batch transform
• Model monitoring: drift detection, SageMaker Model Monitor
• AutoML with SageMaker Autopilot

Study tip: Don’t memorize every algorithm. Instead, learn which algorithm works for which problem type (regression, classification, clustering, etc.). Practice tuning hyperparameters and understanding the trade-offs. Know the difference between SageMaker’s built-in algorithms and using your own container.

Domain 4: Machine Learning Implementation and Operations (20%)

This domain focuses on the operational side: security, cost, and MLOps. It’s about making ML work in production, not just in a notebook.

Key topics:

• SageMaker security (IAM, VPC, KMS encryption)
• SageMaker Pipelines and CI/CD for ML
• Model registry and versioning
• Cost optimization (spot instances, instance selection)
• AWS services like Lambda, Step Functions, and CloudWatch for orchestration and monitoring

Study tip: Learn how to secure a SageMaker notebook instance and how to use lifecycle configurations. Understand how SageMaker Projects integrate with CodeCommit and CodePipeline. Practice setting up a simple pipeline that retrains a model on a schedule.

Take a free AWS Certified Machine Learning - Specialty demo mock to find out where you stand: Try the demo →

How the Exam Is Structured

• Format: 65 questions (60 scored + 5 unscored)
• Duration: 180 minutes (plus 30 minutes for the tutorial)
• Passing score: 750 out of 1000 (scaled)
• Cost: ~$300 USD, but verify on the official portal — AWS Certified Machine Learning - Specialty fees were last revised in early 2026.

The questions are mostly scenario-based. You’ll be given a business problem and asked to pick the best AWS service or ML approach. Some questions have more than one correct answer, but only one is “best” — that’s where the nuance lies.

Study Plan That Works

1. 1Start with the official exam guide. It lists every service and concept you need. Don’t rely on it alone, but use it as a checklist.
2. 2Build a hands-on lab. Use the AWS Free Tier to practice with SageMaker, S3, and Lambda. Even a simple project (e.g., predicting house prices) will solidify the concepts.
3. 3Take practice exams. The real exam is tricky — the questions are long and full of distractors. Practice mocks help you get used to the format and time pressure.
4. 4Review the AWS whitepapers. Specifically, the “AWS Well-Architected Framework” and “Machine Learning on AWS” whitepapers are gold.
5. 5Join a study group or forum. Reddit’s r/AWSCertifications and the AWS Certified LinkedIn group are active and full of tips.

Common Mistakes to Avoid

• Memorizing services without understanding use cases. The exam tests why, not just what.
• Ignoring security. Many candidates lose points on IAM and encryption questions.
• Skipping the free tier. You can’t pass this exam without hands-on experience.
• Not managing time. 180 minutes sounds like a lot, but questions are long. Practice pacing with timed mocks.

See AWS Certified Machine Learning - Specialty mock-test packs and pricing: View plans →

Where to Next?

• Take a free demo mock to gauge your readiness
• Explore the full AWS MLS-C01 mock-test pack
• Read our AWS ML Specialty exam tips and tricks

Remember: the AWS ML Specialty is a professional-level exam. It’s tough, but with the right preparation — hands-on practice, focused study, and realistic mocks — you can pass it. Start with the demo mock, identify your weak areas, and then attack them one by one.