AWS
AWS Certified Machine Learning Engineer - Associate
This certification validates your technical proficiency in building, deploying, and operationalizing production-ready machine learning solutions using AWS services.
What you'll learn
Every objective AWS ML Engineer Associate publishes, and what it asks you to be able to do. The percentage is how much of the exam each one is worth.
- Content Domain 1: Data Preparation for Machine Learning (ML) (28% of the exam)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.
- Content Domain 2: ML Model Development (26% of the exam)Task 2.1: Choose a modeling approach. Task 2.2: Train and refine models. Task 2.3: Analyze model performance.
- Content Domain 3: Deployment and Orchestration of ML Workflows (22% of the exam)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.
- Content Domain 4: ML Solution Monitoring, Maintenance, and Security (24% of the exam)Task 4.1: Monitor model inference. Task 4.2: Monitor and optimize infrastructure and costs. Task 4.3: Secure AWS resources.
The exam at a glance
- Length
- 130 minutes
- Cost
- $150
- Valid for
- 3 years
- Style
- Multiple choice
65 questions. Based on MLA-C01. Pass mark is 720.
Sitting the exam
The part nobody publishes in a syllabus. For AWS ML Engineer Associate the logistics are study strategy: what you are allowed to read while the clock runs changes how you should practise, and what a second attempt costs changes when you should book.
Where you sit it
- Candidates may choose to take their test at a physical Pearson VUE facility or through a remotely monitored session.
- Remote testing is accessible for every certification offered by AWS through the provider Pearson VUE.
How it is marked
- Candidates can expect to see their performance data in their online account within 5 business days of finishing.
Identification and proctoring
- To participate in the exam, you must present a current government-issued ID that proves you live in a country not subject to sanctions.
If you do not pass
- If a candidate does not pass, they are required to wait 14 calendar days before they can attempt the test again.
- Every subsequent attempt at the exam requires the candidate to pay the full price.
Booking it
- The standard price for this certification attempt is 150 USD.
- Changes to an appointment time or cancellations must be completed at least 24 hours before the start time to avoid forfeiting the fee.
- Candidates who are not native speakers of English can get an extra 30 minutes for their session if they request the accommodation before signing up.
Read off the official exam page on 26 August 2026. Rules change without notice, so confirm anything you are about to spend money on.
What the exam covers
Straight from the published curriculum. The weights are how much of the exam each area is worth, so they are the honest guide to where your study time should go.
Course content
17 topics
1. Machine Learning Fundamentals
Common ML algorithms, problem types, and when to apply different approaches
2. Data Engineering Fundamentals
Data formats, storage systems, ETL concepts, and data quality principles
3. AWS Core Services and Security
AWS identity management, data protection, security best practices, and core infrastructure services
4. Software Engineering Best Practices
Version control, testing, CI/CD pipelines, and infrastructure as code principles
5. Amazon SageMaker Fundamentals
SageMaker architecture, core components, and basic workflow for ML projects
6. Data Ingestion and Storage
- Task 1.1: Ingest and store data
7. Data Transformation and Feature Engineering
- Task 1.2: Transform data and perform feature engineering
8. Data Integrity and Model Preparation
- Task 1.3: Ensure data integrity and prepare data for modeling
9. Choosing a Modeling Approach
- Task 2.1: Choose a modeling approach
10. Model Training and Refinement
- Task 2.2: Train and refine models
11. Model Performance Analysis
- Task 2.3: Analyze model performance
12. Selecting Deployment Infrastructure
- Task 3.1: Select deployment infrastructure based on existing architecture and requirements
13. Infrastructure Creation and Scripting
- Task 3.2: Create and script infrastructure based on existing architecture and requirements
14. CI/CD Pipeline Orchestration
- Task 3.3: Use automated orchestration tools to set up continuous integration and continuous delivery (CI/CD) pipelines
15. Model Inference Monitoring
- Task 4.1: Monitor model inference
16. Infrastructure and Cost Optimization
- Task 4.2: Monitor and optimize infrastructure and costs
17. Securing AWS ML Resources
- Task 4.3: Secure AWS resources
What to know before you start
- At least 1 year of experience with Amazon SageMaker and AWS ML engineering services
- Understanding of common ML algorithms and data engineering fundamentals
- Knowledge of software engineering best practices, including CI/CD and infrastructure as code
- Familiarity with AWS security best practices, identity management, and data protection
Other certifications
Frequently asked questions
How long is the AWS ML Engineer Associate exam and what does it cost?
130 minutes, $150. 65 questions. The certification stays valid for 3 years.
What is on the AWS ML Engineer Associate exam?
4 domains. The heaviest is Content Domain 1: Data Preparation for Machine Learning (ML) at 28% of the exam, so it is the one worth over-preparing.
What should I know before starting AWS ML Engineer Associate?
At least 1 year of experience with Amazon SageMaker and AWS ML engineering services. Understanding of common ML algorithms and data engineering fundamentals. Knowledge of software engineering best practices, including CI/CD and infrastructure as code. Familiarity with AWS security best practices, identity management, and data protection.
Is the AWS ML Engineer Associate exam hands-on?
No. It is a written exam, so the skill it tests is recognising the right design or command rather than executing it under time pressure.
Exam details from AWS, checked August 2026. Always confirm on their page before booking.
What learners say about Acelro
About Acelro rather than the AWS ML Engineer Associate exam: what learners made of the gap analysis, the roadmap and the projects.
“The roadmap makes me focus on a learning curve, no matter the length.”
“Acelro has been really great for me, an inspiring experience. I've gained a lot of confidence doing projects I thought were rocket science.”
“The gap analysis maps your actual skills against what the current job market is asking for. Nobody else made it that clear where I stood.”