You can see it againUnder pressure people bring back shapes and positions long after the wording has gone.
Picture superiority · Shepard 1967, Standing 1973
AWSAWS Certified Machine Learning Engineer - Associate
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.
Exam formatMultiple choice, multiple response, ordering, and matching
Duration130 minutes
DeliveryPearson VUE
Passing score720 out of 1000
Free questions368
Content last reviewed 30 July 2026 · Up to date
The certification
What MLA-C01 proves, and what it asks of you
What this certification covers, who it is written for, and what the exam itself looks like on the day.
4domains
12objectives
135concepts
US $150exam fee
What it is
What this certification is
What it validates, who it is written for, and the experience it assumes.
About this certification
The AWS Certified Machine Learning Engineer - Associate (MLA-C01) exam validates your technical ability to implement ML workloads in production and operationalize them on the AWS Cloud. It covers the full ML lifecycle: preparing data for modeling, developing and tuning models, deploying and orchestrating ML workflows, and monitoring, maintaining, and securing ML solutions. The exam is intended for individuals who perform an ML engineer role and demonstrates that you can apply AWS services such as Amazon SageMaker to solve real-world ML engineering challenges.
Earning this certification signals to employers that you possess the hands-on skills needed to build and operate ML systems at scale. It is a role-based Associate-level credential that complements AWS's Specialty certifications, and it is a strong step for professionals looking to advance their careers in machine learning engineering and MLOps.
Who it’s for
This certification is for machine learning engineers, MLOps engineers, and related technical professionals who are responsible for implementing, deploying, and maintaining ML solutions on AWS. The ideal candidate has at least one year of experience using Amazon SageMaker and other AWS services for ML engineering, and at least one year in a related role such as backend software developer, DevOps engineer, data engineer, or data scientist. Professionals who do not yet have prior machine learning experience can still pursue this certification by taking the training available in AWS's Exam Prep Plans and building their knowledge and skills. The exam is designed for those who work hands-on with ML pipelines and want to validate their ability to operationalize ML workloads.
Recommended experience
At least one year of experience using Amazon SageMaker and other AWS services for ML engineering, plus one year in a related role such as backend developer, DevOps engineer, data engineer, or data scientist. Basic understanding of common ML algorithms and their use cases; Data engineering fundamentals, including data formats, ingestion, and transformation for ML pipelines; Knowledge of querying and transforming data; Software engineering best practices for modular, reusable code development, deployment, and debugging; Familiarity with provisioning and monitoring cloud and on-premises ML resources; Experience with CI/CD pipelines and infrastructure as code (IaC); Experience with code repositories for version control and CI/CD pipelines; Knowledge of SageMaker capabilities and algorithms for model building and deployment; Knowledge of AWS data storage and processing services for preparing data for modeling; Familiarity with deploying applications and infrastructure on AWS; Knowledge of monitoring tools for logging and troubleshooting ML systems; Understanding of AWS security best practices for identity and access management, encryption, and data protection
The syllabus
What you’ll learn
Every domain and objective AWS measures, with the weight they carry on the exam.
The official AWS exam outline · checked 30 July 2026 · See the source
Content Domain 1: Data Preparation for Machine Learning (ML)
- 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
- 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
- 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
- Task 4.1: Monitor model inference
- Task 4.2: Monitor and optimize infrastructure and costs
- Task 4.3: Secure AWS resources
On the day
The exam itself
Everything AWS publishes about sitting it, and nothing we inferred.
Prerequisites
No mandatory prerequisites — this certification has no required predecessor exam or credential.
Exam codeMLA-C01
CertificationAWS Certified Machine Learning Engineer - Associate
Exam formatMultiple choice, multiple response, ordering, and matching
Duration130 minutes
Questions50–65 questions
Passing score720 out of 1000
DeliveryPearson VUE
LanguagesEnglish, Japanese, Korean, Simplified Chinese
PricingUS $150
Certification levelAssociate
After you pass
Where this credential goes next
The path AWS lays out, how the credential is kept, and where to book.
Step-by-step path to AWS Certified Machine Learning Engineer - Associate
Credential earnedAWS Certified Machine Learning Engineer - Associate Associate level certification Renewal and maintenance
This certification is valid for 3 years. Before your certification expires, you can recertify by passing the latest version of this exam or completing a recertification assessment on AWS Skill Builder. Stay current with the latest technologies and maintain your certification.
Learn more about renewal requirements Lifecycle status
This certification is currently active and available. AWS maintains this certification to validate current skills and industry relevance.
Exam status: ActiveMaintained by AWS
Exam registration
Register for the exam through Pearson VUE, AWS’s authorized testing partner.
Schedule your examVisit the official AWS certification page for exam policies and requirements.
View the official pageYour coach
And when you are serious, your coach Pip takes over
Your coach in the app reads what you have answered with the book closed and tells you one thing to do tonight. It will not count an answer you gave with the page open, and it will tell you when you are not ready.
See how the coach worksBefore you book
Questions people ask
How is the AWS Certified Machine Learning Engineer - Associate different from the AWS Certified Machine Learning - Specialty?
The Machine Learning Engineer - Associate is a role-based certification designed for ML engineers and MLOps engineers with at least one year of experience in AI/ML. The Machine Learning - Specialty is a specialty certification covering topics across data engineering, data analysis, modeling, and ML implementation and ops, and is more suitable for individuals with 2 or more years of experience developing, architecting, and running ML workloads on AWS.
Do I need to earn a lower-level AWS certification before taking MLA-C01?
No. AWS does not require any formal prerequisites for this exam. You can take MLA-C01 directly, regardless of whether you hold other AWS certifications.
Is there a hands-on or lab component in the MLA-C01 exam?
The MLA-C01 exam consists of multiple choice, multiple response, ordering, and matching questions. There is no separate hands-on lab component in the exam itself, but AWS offers Builder Labs and SimuLearn for hands-on practice during preparation.
What is the retake policy if I fail the MLA-C01 exam?
AWS Certification exams can be retaken after a 14-day waiting period following a failed attempt. There is no limit on the number of attempts, but each attempt requires a new exam fee.
Can I recertify by passing a different AWS exam?
Yes. For Associate-level certifications, you can recertify by passing any Professional-level exam or the latest version of the same exam. For example, passing the AWS Certified Machine Learning - Specialty exam can renew your Associate-level certification.
What job roles does the AWS Certified Machine Learning Engineer - Associate credential map to?
The credential is designed for individuals in roles such as backend software developer, DevOps engineer, data engineer, MLOps engineer, and data scientist who work with ML workloads on AWS.
Are there any regional differences in exam delivery for MLA-C01?
The exam is offered in English, Japanese, Korean, and Simplified Chinese. Testing is available at Pearson VUE testing centers or online proctored, with regional availability varying by language and location.
Information freshness · Content last reviewed on 2026-07-30 Up to date
Practice free questions 368 questions, free, no account needed.
The curriculum
The whole exam, mapped and written
Every domain, every objective, and every concept AWS measures — each one written out.
4domains
12objectives
135concepts
78free pages
The domains, and what sits inside them
Every objective below is a page you can open and practise now, without an account.
The official AWS exam outline · checked 30 July 2026 · See the source
1Content Domain 1: Data Preparation for Machine Learning (ML)80 questions
2Content Domain 2: ML Model Development114 questions
3Content Domain 3: Deployment and Orchestration of ML Workflows115 questions
Task 3.1: Select deployment infrastructure based on existing architecture and requirements13 concepts · 7 pagesOpen →Task 3.2: Create and script infrastructure based on existing architecture and requirements11 concepts · 7 pagesOpen →Task 3.3: Use automated orchestration tools to set up continuous integration and continuous delivery (CI/CD) pipelines22 concepts · 10 pagesOpen →
4Content Domain 4: ML Solution Monitoring, Maintenance, and Security59 questions
Not a table of contents
Every line on this map already opens
In front of every objective the practice pages are already there, free and without an account. This is one objective, opened.
Task 1.1: Ingest and store data6 pagesOpen →
Free in front of it
27 questions on this objective, five to a page. Every range above is a real page, open now, with no account.
Reading the map is not the same as knowing the ground
The curriculum tells you what is on the exam. Proving you know it is a different job — and it is the one the closed-book run does.
Free questions
368 questions. No account, no card.
The whole bank is open. 5 questions to a page, every answer explained, and a discussion thread on each one.
368questions
78pages
12objectives
Freealways
Browse by domain
Every objective, and every page range, is a link — so you can pick up exactly where you left off.
1Content Domain 1: Data Preparation for Machine Learning (ML)80 questions
2Content Domain 2: ML Model Development114 questions
3Content Domain 3: Deployment and Orchestration of ML Workflows115 questions
4Content Domain 4: ML Solution Monitoring, Maintenance, and Security59 questions
What a practice page gives you
1Five to a page
Short enough to finish, long enough to matter.
2Every answer explained
Not only which one is right — why the others are wrong.
3A discussion on each
Ask, answer, and vote. Every question has its own thread.
A question, and the idea behind it
Every question comes from a concept
These are not trivia. Each one is written against a concept in the book, so when you get one wrong there is somewhere to go and find out why.
The visual book
See it once. Carry it into the exam.
The pages shown here come from our AI-900 book — an example of how each concept is written in plain language and, where the idea needs one, drawn as a full page you can take in at a glance.
135concepts
368free questions
Why a picture, and not another paragraph
Three reasons, and each one is a real finding rather than a slogan.
You can see it againThe whole idea at onceWhere it starts, what happens in the middle, what comes out, and the mistake to avoid.
Multimedia principle · MayerThe look-alikes sit togetherThe pairs the exam tests are drawn side by side, so the difference is seen, not told.
Dual coding · PaivioWhat one page is made of
You are never asked to read a poster here — only to see how one is built. After that, every other page is legible at a glance.
1A numbered strip on top
The idea as a sequence, followed with a finger before a word is read.
2Panels for the parts
What it is, how the machine learns it, when it is the right tool.
3The trap, framed
The distinction the exam tests, given its own box instead of buried in prose.
4One line at the bottom
The sentence to carry into the exam room.
Open book, on purpose
Answer it with the picture beside you
This is the part that teaches. The illustration and the written explanation stay where they are while you work, so a scenario stops being a memory test and becomes something you can simply look at.
It teaches · it does not count
A smartphone uses AI to unlock when the owner looks at the camera. Which AI capability is being used?
AImage classification
BFacial recognition
COptical character recognition
DObject detection
Look at the five steps. Detect finds a face, align straightens it, encode turns it into numbers, search compares it with a stored face, and decide answers one question: is this the same person? Classification would only say “a face”. Detection would only draw a box. Neither decides whose.
Then the page shuts, and it starts counting
The same questions come back with the book closed — that run is the one that counts. After it, your coach picks one thing for tonight, sized to the time you have, and brings pages back before you lose them.
Testing effect · Roediger & Karpicke 2006 · spacing effect · Cepeda et al. 2006
Official resources
Straight from AWS
Where the exam is defined, scheduled and scored.
5official links
135concepts mapped
The source
AWS’s own pages
We link to them rather than repeat them, so nothing here goes stale behind them.
Checked against the source
Every objective on the official AWS outline already has questions here
We build from the official skills outline, not from a summary of it — 12 objectives, 135 concepts written under them, and free questions against every one. When AWS changes the outline, this page changes with it.
12objectives on the outline→135concepts written→368free questions
An official page tells you what will be asked. It cannot tell you what you will get right.
That is the only question worth answering the night before, and no link answers it. You answer it by taking the questions with the book closed, and seeing what comes back.