MLA-C01 (English) is no longer available — it retired on September 28, 2026. MLA-C02 has replaced it, and this material has not yet been updated for it.
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Every in-scope SageMaker feature, MLOps workflow, monitoring and security decision the MLA-C01 exam actually tests — organised by the four exam domains with runbooks and decision tables
The AWS Certified Machine Learning Engineer Associate (MLA-C01) is AWS's certification for practitioners who build, deploy, and maintain ML solutions. Launched in 2024, it focuses on the practical engineering side of ML — data pipelines, model training, deployment, monitoring, and MLOps on AWS.
This exam targets ML engineers, data engineers, and software engineers who build and deploy ML models on AWS. Unlike the AI Practitioner (which is conceptual), this certification requires hands-on experience with SageMaker, data pipelines, and model deployment. It's a solid credential for anyone transitioning into ML engineering from a software or data background.
Read the full AWS Machine Learning Engineer Associate certification guide
The MLA-C01 exam has 65 questions (50 scored, 15 unscored) in multiple-choice and multiple-response format. You get 130 minutes, and the passing score is 720/1000 (approximately 72%). The exam costs $150 USD. MLA-C01 retired in English on September 28, 2026; its replacement, MLA-C02, is in beta with 85 questions in 170 minutes at $75 USD.
Recommended experience: One to two years of experience building, deploying, and maintaining ML models, with hands-on experience using Amazon SageMaker and related AWS data services.
Written in the style of the real exam. Answer each one, then open the answer to check.
Question 1 · Data Preparation for ML and AI
A company is building a retrieval augmented generation (RAG) assistant with Amazon Bedrock Knowledge Bases over a library of long technical manuals. Testing shows that searches find the right passage, but the retrieved chunks are so small that the model's answers miss details from the rest of the same section. The company wants to keep precise matching on small passages while giving the model the broader context around each match. Which chunking strategy should the ML engineer configure for the data source?
Answer: D
Option D is correct: hierarchical chunking searches against small child chunks for precise matches and then replaces them with their larger parent chunks in the results, so the model receives the surrounding context. Option A makes each chunk even smaller, which removes more context. Option B gives the model whole manuals with imprecise matching, because one embedding has to represent an entire document. Option C repeats a few tokens between neighboring chunks but does not add the section-level context the answers are missing.
Sources: AWS documentation
Question 2 · ML Model and Foundation Model (FM) Development
An ML engineer trains a computer vision model with an Amazon SageMaker AI training job on a single GPU instance. The training data is 4 TB of image files in Amazon S3. The job uses File input mode, and each run spends a long time copying data to the instance before the first epoch starts. The engineer wants to reduce this startup time without changing how the training code reads files. Which actions will meet these requirements? (Select TWO.)
Answers: B, E
Options B and E are correct: FastFile mode exposes the S3 objects as files that are streamed on demand, so training starts without waiting for a full copy, and FSx for Lustre linked to the bucket mounts the data as a high-throughput file system that the code reads with normal file paths. Option A makes room for the data but the full download still happens before training starts. Option C restricts network access for security and does not speed up data loading. Option D only splits the data across multiple instances, so with a single instance the whole dataset is still downloaded.
Sources: AWS documentation
Question 3 · Deployment and Orchestration of ML and AI Workflows
A company has trained a small scikit-learn model that classifies support tickets. An internal application calls the model synchronously a few hundred times each day in short bursts, with idle periods of several hours between bursts. Users can accept a few extra seconds of latency on the first request after an idle period. Which deployment option will meet these requirements MOST cost-effectively?
Answer: A
Option A is correct: serverless inference scales to zero when there is no traffic and bills only for the compute used to process requests, and it is designed for intermittent traffic that can tolerate cold starts. Option B bills for the instance around the clock, including the long idle periods. Option C has startup times measured in minutes and is meant for offline datasets, not synchronous requests. Option D can scale to zero, but it queues requests and writes results to Amazon S3, so the application would not get a synchronous response.
Sources: AWS documentation 1, AWS documentation 2
These sample questions are not part of the MLA-C01 practice exam, so you won't meet them again when you take it.
This practice exam has 65 questions, and exam mode gives you 130 minutes to answer them. That matches the MLA-C01 exam the questions were written for. MLA-C01 has retired in English, and its replacement, MLA-C02, has 85 questions in 170 minutes. Practice mode has no timer.
In format, yes: questions are multiple choice (one correct answer) or multiple response (two or more), spread across the 4 exam domains in close to the weights the official exam guide gives them. Most of that content carries over to MLA-C02, which adds Amazon Bedrock, RAG, agents and generative AI evaluation that you will need to study separately. Scoring differs: AWS reports a scaled score from 100 to 1,000 and you pass at 720, while this practice exam shows the raw percentage you got right. For how hard the exam is, whether it is worth it and how long to study, read our AWS Machine Learning Engineer Associate certification guide.
Aim for 80% or more, consistently, on full-length attempts in exam mode, not just once. That leaves a margin over the 720 out of 1,000 you need on the real MLA-C01 exam, whose questions will all be new to you. Repeat attempts here show the same questions, so judge yourself by first attempts and by whether you can explain each answer. No practice score guarantees a pass.
No. The MLA-C01 practice exam is free and needs no account. Your progress and score history are saved in this browser, so they stay on this device and are lost if you clear your browser data.
Start in practice mode, which tells you after each question whether you were right and explains the answer. When you score well there, switch to exam mode: 130 minutes and no feedback until the end, like test day. Your results break the score down by domain, so you can see which domains to study before your next attempt.
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