AWS Certified Machine Learning Engineer - Associate (MLA-C01) Study Guide [160 Minute Read]

A First-Principles Approach to Machine Learning Engineering on AWS

Welcome to the AWS Certified Machine Learning Engineer - Associate (MLA-C01) Study Guide. This guide moves beyond surface-level memorization. It is designed to build a robust mental model of how machine learning systems are built, deployed, and maintained on AWS—understanding the why behind every architectural decision, service selection, and operational trade-off.

Each topic is aligned with the official AWS MLA-C01 Exam Objectives, targeting the specific cognitive skills required for success. Expect scenario-based questions that test your ability to choose the right AWS service for a given ML workflow stage, troubleshoot pipeline failures, and make cost-performance trade-offs—roughly 60% application, 30% analysis, and 10% recall.

Exam Details: 65 questions (50 scored, 15 unscored) — Multiple choice, multiple response, ordering, matching, case study | 170 minutes | Passing score: 720/1000

Prerequisites: At least 1 year of experience using Amazon SageMaker and other AWS services for ML engineering. Familiarity with common ML algorithms, data engineering fundamentals, CI/CD pipelines, and software engineering best practices. Background in a role such as backend developer, DevOps engineer, data engineer, or data scientist is assumed.

Exam Domain Weights

Domain 1 (Data Preparation) carries the heaviest weight at 28%, reflecting the reality that most ML engineering effort goes into getting data right before models ever train. Combined with Domain 4's 24% on monitoring and security, over half the exam tests your ability to handle what happens around the model—not just the model itself. Prioritize these operational areas alongside model development.

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