AWS Certified Machine Learning Engineer, Associate (MLA-C01)

AWS Machine Learning Engineer Associate Practice Exam: MLA-C01 Certification Practice Questions

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The AWS Certified Machine Learning Engineer, Associate exam (MLA-C01) is 65 questions in 130 minutes, you need a scaled score of 720 out of 1000 to pass, it costs US$150, and it has no hard prerequisites. It is an associate-tier, MLOps-focused exam covering the full machine learning lifecycle on AWS: preparing data, developing models, deploying and orchestrating ML workflows, and monitoring, maintaining, and securing them. AWS recommends about a year of Amazon SageMaker experience. The credential is valid for three years. Note the version change below: MLA-C02 registration opens September 1, 2026 and MLA-C01 retires in English on September 28, 2026.

Last updated July 2026

MLA-C01 is moving to MLA-C02: what changes and when

AWS is updating this exam, and the timing matters if you are planning when to sit it. Registration for the new MLA-C02 exam opens September 1, 2026, and the last day to take MLA-C01 in English is September 28, 2026. MLA-C02 begins as an English-only beta and later adds Japanese, Korean, and Simplified Chinese. The certification name and associate level do not change; only the exam version code moves from C01 to C02.

What this means for you is simple. If you are ready in the next couple of months, take MLA-C01 and earn the exact same credential, valid for three years. If you are further out, you will sit MLA-C02, and AWS has not yet published its domain weights or question details, so confirm them on the official AWS Certification page when they land. The underlying skills, data preparation, model development, deployment and orchestration, and monitoring, are the core of an ML engineer's job and carry across both versions, so time spent studying now counts toward either exam.

What the AWS Machine Learning Engineer Associate exam tests

MLA-C01 is organized into four domains, and the weights are close, so no single area dominates. This is a broad MLOps exam: it walks the full machine learning lifecycle from raw data to a monitored production model. Here is what each domain actually asks, so you can see where your hands-on experience covers you and where it does not.

Domain Weight What is actually in it
Data Preparation for ML28%The heaviest domain. Ingesting, cleaning, transforming, and feature-engineering data, handling data formats and storage, and building repeatable data pipelines for training. Expect questions on choosing the right AWS data and feature tooling for a described dataset.
ML Model Development26%Choosing algorithms and frameworks, training and tuning models with SageMaker, evaluating performance, and managing experiments. Scenario questions on selecting and improving a model for a stated objective and constraint.
Deployment and Orchestration of ML Workflows22%Deploying models to endpoints, batch and real-time inference, automating pipelines, and orchestrating the ML workflow with the right AWS services. The MLOps core where many candidates have gaps.
ML Solution Monitoring, Maintenance, and Security24%Monitoring for drift and quality, retraining, cost and performance tuning, and securing ML systems and data. Questions ask how to keep a deployed model reliable, compliant, and affordable over time.

Because every domain is scenario-based and the weights are even, the fastest way to prepare is to practice across all four. Generate practice questions from your own AWS ML notes and give extra attention to deployment, orchestration, and monitoring, the operational domains where hands-on gaps most often cost points.

Exam fee
US$150
Format
65 Q / 130 min
Passing score
720 / 1000
Level
Associate

Why drill questions for the AWS ML Engineer Associate?

Because this exam rewards engineering judgment across the whole ML lifecycle, and that judgment is uneven for most people. You might build models every day but rarely set up monitoring, or run pipelines but seldom tune the data-preparation stage. The exam does not care which part of the job you specialize in; it tests all four domains. Practice questions that force decisions across data prep, modeling, deployment, and monitoring are the fastest way to widen that range before you sit it.

Where candidates lose points

Deployment and orchestration, monitoring for drift, and securing ML systems. Data scientists underestimate the MLOps and operations domains; platform engineers underestimate model development and evaluation. Drill across all four domains and you cover where compensatory scoring lets a single weak area drag your scaled score under 720.

Match the scenario format

Generate questions that read like the exam: a requirement, a constraint on cost or latency, and several plausible AWS approaches where only one is best. That trains you to eliminate the answer that works but is expensive, insecure, or hard to operate, which is exactly the discrimination MLA-C01 tests. Confirm the current domain weighting on the official AWS exam guide so your practice mirrors the real split.

A US$150 exam plus study time is a real investment, and this one expects genuine hands-on MLOps experience. Uploading your notes and generating questions across all four domains is cheap insurance that you are ready, rather than practicing only the modeling work you already enjoy.

Where ML Engineer Associate sits in the AWS path

AWS has four tiers: foundational, associate, professional, and specialty. On the AI and machine learning track, the foundational AI Practitioner introduces the concepts, and the associate ML Engineer proves you can build and operate ML systems. Here is how the AI and ML credentials compare so you pick the right target.

  AI Practitioner ML Engineer Associate Developer Associate
Exam codeAIF-C01MLA-C01DVA-C02
TierFoundationalAssociateAssociate
Format65 Q, 90 min65 Q, 130 min65 Q, 130 min
Pass700 / 1000720 / 1000720 / 1000
CostUS$100US$150US$150
Best forUsing AI on AWSBuilding ML in productionBuilding apps on AWS

Newer to AI on AWS? Start with the AWS AI Practitioner practice exam, the foundational entry point whose credential this associate exam also renews. Coming from application development? The AWS Developer Associate practice exam shares the same associate format and pairs well with an ML engineering role.

How to build AWS ML Engineer Associate practice questions that match the exam

The exam tests the full ML lifecycle across four domains. Your questions should drill exactly that.

1
Upload MLA-C01 material
Feed in notes built on the current MLA-C01 domains and the AWS ML services they name, chiefly SageMaker. Confirm the domain weighting on the official AWS study guide, and check for MLA-C02 updates if you test after September 2026.
2
Practice as scenarios
Generate questions that give a requirement and a cost or latency constraint, with several plausible AWS approaches. That trains the elimination skill the exam tests, where more than one answer works but only one is the best operational build.
3
Weight deployment and monitoring
Put extra practice on deployment, orchestration, drift monitoring, and securing ML systems. These operational domains are where working data scientists most often have blind spots and where the exam probes hardest.
4
Retake until it clicks
Regenerate fresh sets and retake until the right AWS approach for a described scenario comes back instantly. Aim to clear practice sets comfortably above the 720 bar before you book.

AWS Machine Learning Engineer Associate exam questions, answered

Is MLA-C01 being retired, and should I wait for MLA-C02?
The credential is not retiring, but the exam version is changing. Registration for MLA-C02 opens September 1, 2026, and the last day to take MLA-C01 in English is September 28, 2026. If you are ready now, take MLA-C01 and earn the same certification, valid for three years. If you are months away, you will likely sit MLA-C02. The skills tested carry over, so studying now is not wasted.
Is the AWS ML Engineer Associate exam hard?
It is a genuine associate-level exam and harder than the foundational AI Practitioner. It expects hands-on familiarity with Amazon SageMaker and the ML lifecycle: preparing data, training and tuning models, deploying and orchestrating workflows, and monitoring models in production. AWS recommends about a year of SageMaker experience plus a year in a related role. If you build ML systems, it is manageable with focused study. If you only use AI tools, budget more time and hands-on practice.
MLA-C01 vs the old ML Specialty (MLS-C01)?
MLA-C01 is the associate Machine Learning Engineer exam focused on operationalizing ML: data preparation, model development, deployment and orchestration, and monitoring, maintenance, and security. The older MLS-C01 Machine Learning Specialty was a specialty-tier exam that leaned more toward modeling and data science theory. MLA is more engineering and MLOps oriented and sits one tier lower. For most engineers building and running ML pipelines on AWS, MLA is the more current and relevant credential.
How much SageMaker experience do I need?
AWS recommends at least one year of hands-on experience with Amazon SageMaker and other AWS ML services, plus at least one year in a related role such as backend developer, DevOps engineer, data engineer, or data scientist. There is no hard prerequisite, so you can sit the exam without meeting that guidance, but the questions assume you have actually built, deployed, and monitored models on SageMaker rather than just read about it.
What is the passing score and cost of MLA-C01?
The exam costs US$150 and you need a scaled score of 720 out of 1000 to pass. AWS uses compensatory scoring, so you do not need to pass each domain individually. There are 65 questions, of which 50 are scored and 15 are unscored research questions that are not marked during the exam. The credential is valid for three years.
How long should I study for the exam?
Most candidates with ML engineering experience need four to eight weeks of part-time study. If you work with SageMaker daily, you may need less; if ML operations are new to you, budget more and spend it on the deployment, orchestration, and monitoring domains, where hands-on gaps show. Practicing scenario questions from your own notes across all four domains is the fastest way to find and close those gaps before the US$150 exam.

PDFQuiz is not affiliated with, endorsed by, or sponsored by Amazon Web Services. AWS and Amazon Web Services are trademarks of Amazon.com, Inc. or its affiliates. This generator builds practice questions from material you upload and is a study aid, not a substitute for the official exam guide or training. Exam details change, so always confirm current details on the AWS Certification page before you book.

Related study tools

Building your AWS AI path? The AWS AI Practitioner practice exam is the foundational entry point this credential renews, and the AWS Developer Associate practice exam shares the same associate format. Any study guide works with the certification exam generator, or start from any PDF with the PDF to practice test generator.

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