AWS Certified Machine Learning Engineer – Associate
Exam overview, official domain weights and free sample questions. Subscribers get 230 practice questions, 106 flashcards, 2 labs, a study guide and video lessons for this exam.
Exam at a glance
- Exam code
- MLA-C02
- Questions
- Beta: 85. GA: 65 (50 scored + 15 unscored)
- Time
- Beta: 170. GA: not published (MLA-C01 was 130) minutes
- Question types
- multiple choice, multiple response
- Passing score
- 720 (scaled 100–1,000). Beta results are reported differently
- Exam fee
- Beta: 75 USD. GA: not published (MLA-C01 was 150 USD)
- Delivery
- Pearson VUE test center, online proctored
- Prerequisites
- None. Recommended: 1+ year with SageMaker AI, Amazon Bedrock, and related services, plus 1+ year in a related role
- Renewal
- Valid 3 years. Recertify by passing the latest version
- Version notes
- MLA-C01 English: last day Sep 28, 2026. MLA-C01 Japanese, Korean, and Simplified Chinese remain until MLA-C02 GA. MLA-C02 beta delivery began Sep 29, 2026. GA registration and delivery dates TBD
From the official exam page (checked 2026-11-09). Always confirm details with the vendor: official exam page, official objectives.
Domains and weights
- 1.1 Collect and store data
- 1.2 Perform data transformation, feature engineering, and pre-processing
- 1.3 Validate data quality and manage bias
- 2.1 Choose appropriate modeling approaches for ML and AI solutions
- 2.2 Train, fine-tune, and customize models for ML and AI solutions
- 2.3 Analyze and evaluate the performance of ML and AI systems
- 3.1 Manage deployment infrastructure for ML and AI model types
- 3.2 Provision and configure resources for ML and AI workloads based on existing architecture and requirements
- 3.3 Implement automated orchestration and CI/CD pipelines for MLOps and AI workloads
- 4.1 Monitor ML and AI model inference and performance
- 4.2 Optimize and manage ML and AI infrastructure costs and performance
- 4.3 Secure ML and AI workloads and model endpoints
Free sample questions
Original questions in the style of the exam. Pick an answer to see the explanation.
A team stores 4 TB of clickstream data as CSV in Amazon S3 and runs Athena queries that scan only 5 of 120 columns. Queries are slow and costly. What is the best change?
A company needs to classify customer emails into 6 categories. It has no ML team, 2,000 labeled emails, and needs a working solution in weeks. Which approach is best?
An organization wants to use a model it trained outside AWS (open-weight architecture) within Amazon Bedrock's serverless API. What feature supports this?
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