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Before you begin studying for the AWS Certified Machine Learning Engineer — Associate exam, the single most important step is to read the official exam guide. The guide is the canonical source for exam structure, objectives, domain weights, and the AWS services that are in-scope—use it to plan and prioritize your study. Open the official exam guide and review:
  • Exam purpose and target candidate profile
  • Domains and domain weights
  • Question formats and scoring rules
  • The appendix listing AWS services and features you should know
This image shows the first page of the AWS Certified Machine Learning Engineer - Associate exam guide. It includes an introduction and target candidate description.
Read the official exam guide first — it defines the domains, exam objectives, question types, scoring, and the AWS services referenced for the certification.
What the guide contains
  • A short introduction describing the exam objectives and the kinds of tasks you will be expected to perform.
  • A breakdown of responsibilities and skills: data ingestion, transformation, validation, and feature preparation; model selection and training; deployment choices; CI/CD and orchestration; monitoring, observability, and security for ML systems.
  • A list of recommended hands-on experience and prerequisite knowledge so you can identify any skill gaps to target in your study plan.
The image displays a PDF document titled "AWS Certified Machine Learning Engineer - Associate," detailing the introduction and targeted candidate description for the certification exam.
Target candidate and recommended experience The guide explains the expected background of typical candidates. For this certification, AWS recommends roughly one year of hands-on experience with Amazon SageMaker and about one year in related roles (backend development, DevOps, or data engineering). Use the recommendations to map your current skills against the exam scope and prioritize practical labs for areas where you lack experience.
The image shows a document listing the recommended AWS knowledge for a Certified Machine Learning Engineer, including familiarity with SageMaker, data processing services, and security best practices. The document also outlines job tasks that are out of scope for the target candidate.
Exam question types and format
  • Multiple choice (single correct answer)
  • Multiple response (more than one correct answer)
  • Ordering and matching (less common)
  • Case study / scenario-based questions
Note: the exam may include unscored (pretest) questions mixed into the live exam. These cannot be identified by candidates and are used by AWS to validate future exam items.
The image shows a document detailing the exam content for the AWS Certified Machine Learning Engineer Associate exam, including question types like multiple choice, multiple response, and case study.
Up to 15 questions may be unscored pretest items. Focus on answering every question to the best of your ability — you won’t know which are scored.
Scoring and domain weights
  • Exam score range: 100–1000
  • Minimum passing score: 720
The exam is organized into four domains. Below is a quick reference of domain names and weights to help you prioritize study time: Focusing on the top two domains (Data preparation and ML model deployment) covers more than half of the exam content, but don’t neglect deployment, monitoring, and security—these topics are tested heavily as well. Appendix: in-scope AWS services The guide’s appendix lists AWS services grouped by category (analytics, application integration, security, ML, etc.). Use it to plan hands-on practice with the specific technologies called out—examples include Amazon Athena, AWS Glue, Amazon Kinesis, and Amazon SageMaker.
The image shows a PDF document titled "AWS Certified Machine Learning Engineer - Associate," specifically an appendix listing AWS services and features related to analytics. The document includes services like Amazon Athena, AWS Glue, and Amazon Kinesis.
How to use the exam guide to plan your study
  1. Map your experience: compare your hands-on exposure to the domains and the listed AWS services.
  2. Prioritize study time: allocate more time to the domains with higher weights and to services you’re less familiar with.
  3. Build a hands-on plan: complete labs that cover data processing (Glue, Athena, Kinesis), model development & tuning (SageMaker), and deployment & monitoring (SageMaker Endpoints, CI/CD, CloudWatch).
  4. Practice exam-style questions and full-length timed practice tests to get used to scenario-based questions and time management.
Links and references Use the exam guide as the backbone of your study plan, keep a practical, hands-on focus, and allocate study time according to the domain weights and service lists identified in the guide. Good luck with your preparation!

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