The AWS Certified Machine Learning Engineer - Associate (MLA-C01) exam is one of the most practical ML certifications AWS offers. It doesn't ask you to memorize algorithms — it asks you to build, train, deploy, and monitor ML models on AWS. This page breaks down the official syllabus into four exam domains, gives you realistic weightage estimates, and tells you exactly how to prepare for each section.
If you're short on time, here's the honest summary: the exam is 50% about knowing which AWS service to use for which job, 25% about hands-on troubleshooting, and 25% about understanding ML lifecycle basics. You'll need to know SageMaker deeply, but also understand how Lambda, Step Functions, and even plain S3 fit into an ML pipeline.
Exam Overview: What to Expect
- Format: 65 questions (50 scored + 15 unscored)
- Time: 130 minutes
- Cost: $150 (verify on the official AWS portal — fees were last revised in early 2026)
- Passing score: Around 720/1000 (AWS doesn't publish exact cutoffs)
- Question types: Multiple choice, multiple response, and case-based scenarios
The exam is scenario-heavy. You'll be given a business problem and a set of constraints (cost, latency, data size) and asked to pick the best ML solution. This is not a theory test.
Domain 1: Data Engineering for ML (Weightage: ~20%)
This domain tests your ability to prepare data for training and inference. You need to know how to build data pipelines that are reliable, repeatable, and secure.
What You Must Know
- S3 for ML data: Bucket structures, partitioning, and lifecycle policies for training data
- AWS Glue / Glue DataBrew: Crawling, cataloging, and cleaning data
- Amazon EMR & Athena: When to use distributed processing vs. simple SQL queries over data lakes
- Feature engineering on AWS: Using SageMaker Data Wrangler, Feature Store, and processing jobs
- Data security: Encryption at rest and in transit, IAM roles for data access
Study Tips
- Practice building a simple ETL pipeline with Glue and Athena on a sample dataset.
- Understand the difference between a Glue job, a SageMaker Processing job, and an EMR step — you'll get at least one question asking you to pick one.
- Know when to use SageMaker Feature Store vs. just storing features in S3. Think about online vs. offline feature retrieval.
Take a free AWS Certified Machine Learning Engineer - Associate demo mock to find out where you stand: Try the demo →
Domain 2: Exploratory Data Analysis (Weightage: ~20%)
This domain is about understanding your data before you build a model. You need to know how to use AWS tools to visualize, bias-check, and validate data quality.
What You Must Know
- SageMaker Data Wrangler: Visualizing distributions, handling missing values, and creating transformations
- Amazon Athena & QuickSight: Running ad-hoc queries and building dashboards for data exploration
- Bias detection: Using SageMaker Clarify to detect bias in training data and model predictions
- Data quality checks: Setting up SageMaker Model Monitor to detect data drift after deployment
Study Tips
- Know the difference between data drift, concept drift, and bias. You'll see all three terms.
- Understand how to use SageMaker Clarify's bias metrics (e.g., difference in positive proportions, disparate impact).
- Practice interpreting a confusion matrix and ROC curve — they appear in scenario questions.
Domain 3: Modeling (Weightage: ~35%)
This is the biggest domain. It covers everything from choosing the right algorithm to training, tuning, and evaluating models.
What You Must Know
- Built-in algorithms vs. custom models: When to use SageMaker built-in algorithms (XGBoost, Linear Learner, etc.) vs. bringing your own container
- Training on SageMaker: Setting up training jobs, using managed spot training, and choosing instance types
- Hyperparameter tuning: Using SageMaker Automatic Model Tuning (AMT) with Bayesian optimization or random search
- Model evaluation: Cross-validation, metrics like precision/recall/AUC, and using SageMaker Experiments to track runs
- AutoML: When to use SageMaker Autopilot vs. manual modeling
Study Tips
- You don't need to know the math behind every algorithm, but you should know when to use each one. For example, XGBoost for tabular data, BlazingText for text classification, and Object2Vec for recommendation.
- Understand the difference between a training job, a tuning job, and a transform job. This is a common source of confusion.
- Practice reading a SageMaker training log to debug a failed job — the exam loves to show you a CloudWatch log snippet and ask what went wrong.
Domain 4: ML Implementation and Operations (Weightage: ~25%)
This domain is about taking a trained model and putting it into production — and keeping it there. It's often the most underestimated part of the exam.
What You Must Know
- Deployment options: SageMaker endpoints (real-time), batch transform, serverless inference, and multi-model endpoints
- CI/CD for ML: Using SageMaker Pipelines, AWS CodePipeline, and Step Functions to automate training and deployment
- Monitoring: SageMaker Model Monitor for data quality, model quality, and bias drift
- Security & compliance: IAM, VPC, and KMS for protecting models and data
- Cost optimization: Choosing the right instance type, using auto-scaling, and leveraging spot instances
Study Tips
- Know the difference between a real-time endpoint and a batch transform job. The exam often asks which one to use based on latency and cost.
- Understand how to set up a SageMaker Pipeline that retrains a model on a schedule and deploys it to a staging endpoint.
- Practice troubleshooting a broken endpoint — e.g., missing IAM role permissions or incorrect model artifacts.
See AWS Certified Machine Learning Engineer - Associate mock-test packs and pricing: View plans →
How to Prepare: A Week-by-Week Plan
If you have 4–6 weeks, here's a realistic schedule:
- Week 1: Data engineering + EDA. Use AWS free tier to build a simple S3 + Glue + Athena pipeline.
- Week 2: Modeling. Train an XGBoost model on SageMaker using a public dataset (e.g., Titanic or Credit Card Fraud).
- Week 3: Operations. Deploy the model to a real-time endpoint, set up monitoring, and write a simple Lambda function to invoke it.
- Week 4: Practice exams. Take a full-length mock test every other day, review your weak areas, and re-read the official exam guide.
Common Mistakes to Avoid
- Over-studying algorithms: The exam is not a data science theory test. Focus on AWS services and ML lifecycle.
- Ignoring IAM and security: Expect at least 3–5 questions on IAM roles, policies, and encryption.
- Not practicing with the AWS console: You can't pass this exam without hands-on experience. Spend at least 10 hours in SageMaker.
Official Resources and Where to Get Them
- AWS Exam Guide (PDF): The official guide lists all domains and objectives. Download it from the AWS certification page.
- AWS Skill Builder: Free and paid courses, including the official "Exam Readiness" course.
- AWS Documentation: SageMaker, Glue, and Athena docs are your best friends. Read the "How it works" sections.
- Practice exams: AWS offers a free sample test, but it's short. Use third-party mock tests for realistic length and difficulty.