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AWS Certified Machine Learning Engineer - Associate

MLA-C01

The AWS Certified Machine Learning Engineer - Associate certification validates your ability to build, operationalize, deploy, and maintain machine learning solutions and pipelines on AWS. It is designed for ML engineers and MLOps practitioners with at least one year of hands-on experience using Amazon SageMaker and other AWS ML services. Earning this credential positions you for in-demand machine learning roles and demonstrates that you can turn ML models into reliable, production-grade workloads.

Practice Free Questions

4Domains

12Objectives

78Practice pages

368Free questions

MLA-C01 free practice questions

Choose any objective and open any question page — no sign-in required.

Task 1.1: Ingest and store data

27 questions · 6 pages

Questions 1–5 6–10 11–15 16–20 21–25 26–27

Task 1.2: Transform data and perform feature engineering

25 questions · 5 pages

Questions 1–5 6–10 11–15 16–20 21–25

Task 1.3: Ensure data integrity and prepare data for modeling

28 questions · 6 pages

Questions 1–5 6–10 11–15 16–20 21–25 26–28

Task 2.1: Choose a modeling approach

32 questions · 7 pages

Questions 1–5 6–10 11–15 16–20 21–25 26–30 31–32

Task 2.2: Train and refine models

43 questions · 9 pages

Questions 1–5 6–10 11–15 16–20 21–25 26–30 31–35 36–40 41–43

Task 2.3: Analyze model performance

39 questions · 8 pages

Questions 1–5 6–10 11–15 16–20 21–25 26–30 31–35 36–39

Task 4.1: Monitor model inference

19 questions · 4 pages

Questions 1–5 6–10 11–15 16–19

Task 4.2: Monitor and optimize infrastructure and costs

27 questions · 6 pages

Questions 1–5 6–10 11–15 16–20 21–25 26–27

Task 4.3: Secure AWS resources

13 questions · 3 pages

Questions 1–5 6–10 11–13

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Percentages reflect share of the current practice bank, not official exam weightings. Every page above is a live, crawlable practice page (12 objectives · 78 pages).

AWS Certified Machine Learning Engineer - Associate

MLA-C01 free practice questions

Task 1.1: Ingest and store data

Task 1.2: Transform data and perform feature engineering

Task 1.3: Ensure data integrity and prepare data for modeling

Task 2.1: Choose a modeling approach

Task 2.2: Train and refine models

Task 2.3: Analyze model performance

Task 3.1: Select deployment infrastructure based on existing architecture and requirements

Task 3.2: Create and script infrastructure based on existing architecture and requirements

Task 3.3: Use automated orchestration tools to set up continuous integration and continuous delivery (CI/CD) pipelines

Task 4.1: Monitor model inference

Task 4.2: Monitor and optimize infrastructure and costs

Task 4.3: Secure AWS resources

AWS Certified Machine Learning Engineer - Associate

MLA-C01 free practice questions

Domain 1 · Content Domain 1: Data Preparation for Machine Learning (ML)22% of practice bank · 3 objectives · 80 questions22%

Domain 2 · Content Domain 2: ML Model Development31% of practice bank · 3 objectives · 114 questions31%

Domain 3 · Content Domain 3: Deployment and Orchestration of ML Workflows31% of practice bank · 3 objectives · 115 questions31%

Domain 4 · Content Domain 4: ML Solution Monitoring, Maintenance, and Security16% of practice bank · 3 objectives · 59 questions16%