AWS Certified Machine Learning Engineer – Associate (MLA-C01) Practice Exam
Cover all four MLA-C01 domains — Data Preparation, ML Model Development, Deployment and Orchestration, and Monitoring, Maintenance and Security — with objective-mapped, SageMaker-focused practice questions, immediate feedback in Learn mode, and full timed simulation in Exam mode.
Start 24-hour free trial →MLA-C01 exam at a glance
- Vendor
- Amazon Web Services (AWS)
- Exam code
- MLA-C01
- Certification
- AWS Certified Machine Learning Engineer – Associate
- Level
- Associate
- Blueprint
- MLA-C01 exam guide; generally available since October 2024 (current edition)
- Format
- 65 questions (50 scored + 15 unscored); multiple-choice, multiple-response, plus ordering, matching, and case-study items
- Duration
- 130 minutes
- Passing score
- 720 on a scaled range of 100–1000 (pass/fail, compensatory scoring)
- Delivery
- Pearson VUE test center or online proctored
- Recommended experience
- At least 1 year in ML engineering or a related role (backend developer, DevOps, data engineer, or data scientist), plus hands-on AWS/SageMaker exposure
- Cost (USD)
- $150 USD (50% discount voucher available after any prior AWS certification)
- Validity
- 3 years; recertify by passing the current exam or a higher-level AWS certification
- Languages
- English, Japanese, Korean, and Simplified Chinese (verify current list with AWS)
Source: AWS — MLA-C01 Exam Guide (domains & weightings). Verify current details with AWS before scheduling.
About the AWS Certified Machine Learning Engineer – Associate certification
AWS Certified Machine Learning Engineer – Associate (MLA-C01) is AWS’s associate-level credential for people who operationalize machine learning — not just train models in a notebook, but build, deploy, monitor, and maintain production ML systems on AWS. The exam is built around Amazon SageMaker and the MLOps lifecycle: ingesting and preparing data, developing and tuning models, orchestrating pipelines, and keeping deployed models healthy, secure, and cost-effective.
MLA-C01 reached general availability in October 2024 as AWS’s modern, role-based answer to the retiring Machine Learning – Specialty (MLS-C01). The difference in emphasis is the point: MLS-C01 leaned toward ML theory and algorithm selection, while MLA-C01 is more practical and operations-focused — feature engineering, pipeline orchestration, model monitoring, and deployment patterns. Its heaviest area is Data Preparation for ML (28%). For the underlying services, see the AWS AI/ML services deep dive.
Every PowerKram practice question maps to one of the four current MLA-C01 domains and links to the specific AWS documentation page it was derived from, so your weak spots become a focused reading list rather than a guess.
MLA-C01 exam domains and weights
Four domains, with Data Preparation for ML the single heaviest area. The weightings are AWS’s own; because scoring is compensatory, you pass on the overall scaled score rather than needing a minimum in each domain. Plan your study time roughly in proportion to these weights.
Ingesting and storing data (S3, Glue, EMR, Kinesis); transforming and engineering features (SageMaker Data Wrangler, Feature Store, Glue DataBrew); and ensuring data integrity, quality, and bias detection (SageMaker Clarify) before training.
Choosing a modeling approach and SageMaker built-in algorithms; training and refining models; hyperparameter tuning (SageMaker automatic model tuning); and evaluating model performance with the right metrics.
Monitoring models and data for drift (SageMaker Model Monitor, CloudWatch); maintaining and retraining pipelines; and securing ML systems and data with IAM, encryption, and access controls.
Selecting deployment infrastructure and endpoints; provisioning compute for training and inference; and automating end-to-end ML pipelines with SageMaker Pipelines, Step Functions, and CI/CD.
Source: AWS MLA-C01 Exam Guide — content domains and weightings. Weightings indicate relative exam emphasis; some sections carry more questions than others.
Who MLA-C01 is for
AWS positions Machine Learning Engineer – Associate for people who build and run ML systems in production, not only data scientists who train models. The audience is engineering-centric:
- ML engineers and MLOps engineers who build, deploy, and maintain production ML pipelines on AWS and want a credential that validates operational depth.
- Data engineers moving into ML pipeline development — the 28% data-preparation domain plays directly to existing strengths.
- Backend and DevOps engineers taking on model deployment, orchestration, and monitoring responsibilities.
- Data scientists who want to prove they can operationalize models, not just develop them.
AWS recommends about a year of ML-engineering or related experience plus hands-on SageMaker exposure. If AI/ML fundamentals are still new, the AI Practitioner (AIF-C01) is a gentler on-ramp, and the data-focused Data Engineer – Associate (DEA-C01) pairs naturally given how much of MLA-C01 is data preparation. For the roles this credential supports — with skills, tools, and salary ranges — see the AI Engineer career path and where AWS ML skills lead.
What this MLA-C01 practice exam delivers
Learn mode
Get the correct answer, the explanation, and a direct link to the exact AWS documentation page each question was derived from — immediately after each question. Best for the SageMaker-heavy data-prep and model-development areas, where service-to-task matching is the main testing pattern.
Exam mode
65 questions, 130-minute timer — the real MLA-C01 format. Build pacing for the scenario questions and the newer ordering, matching, and case-study formats AWS introduced with this exam.
Source-linked explanations
Every answer cites the exact AWS page (docs.aws.amazon.com) the question was built from — so you can verify SageMaker service behavior and dig deeper, not just memorize.
Score by MLA-C01 domain
Results break down by the four blueprint domains — Data Preparation, Model Development, Monitoring/Maintenance/Security, and Deployment/Orchestration — so practice tells you exactly which area to revisit.
Sample MLA-C01 practice questions
Ten free questions across the four current MLA-C01 domains, with full explanations and source links to the AWS pages each is derived from. The complete bank is available with the 24-hour trial.
A team needs a visual, low-code way to explore, clean, and engineer features from tabular training data inside SageMaker before model training. Which SageMaker capability fits best?
- SageMaker Data Wrangler
- SageMaker Model Monitor
- SageMaker Neo
- SageMaker Ground Truth
Show answer & explanation
Correct: A — SageMaker Data Wrangler. Data Wrangler provides a visual, low-code interface to import, explore, clean, and engineer features from tabular data, then export a repeatable data-prep flow — exactly the pre-training data-preparation task described.
Why not the others: Model Monitor (B) watches deployed models for drift, not data prep; Neo (C) compiles models for optimized inference; Ground Truth (D) is for data labeling, not feature engineering on existing tabular data.
Source: AWS — SageMaker Data Wrangler → Further reading: PowerKram — AWS AI/ML services deep dive →A team wants to store, share, and reuse curated features across multiple training jobs and for real-time inference, keeping offline and online feature values consistent. Which SageMaker capability provides this?
- Amazon S3 raw buckets alone
- SageMaker Feature Store
- SageMaker Debugger
- Amazon Athena views
Show answer & explanation
Correct: B — SageMaker Feature Store. Feature Store is a purpose-built repository to store, share, and reuse features, with both offline (training) and online (low-latency inference) stores kept consistent — the described requirement.
Why not the others: raw S3 buckets (A) give storage but no feature management or online/offline consistency; Debugger (C) inspects training runs; Athena views (D) query data but do not serve features for real-time inference.
Source: AWS — SageMaker Feature Store →Before training, a team must check whether their training dataset over-represents certain groups so the model does not learn a skewed pattern. Which SageMaker capability detects this data bias?
- SageMaker Autopilot
- SageMaker Edge Manager
- SageMaker Clarify
- SageMaker JumpStart
Show answer & explanation
Correct: C — SageMaker Clarify. Clarify detects potential bias in data (and in models), computing bias metrics across groups before and after training, and supports explainability — the tool for the pre-training data-bias check described.
Why not the others: Autopilot (A) automates model building; Edge Manager (B) manages models on edge devices; JumpStart (D) provides pre-built models and solutions — none is the bias-detection tool.
Source: AWS — Detect data bias with SageMaker Clarify → Further reading: PowerKram — responsible ML on AWS →A developer needs a fast, scalable built-in SageMaker algorithm for a structured-data classification and regression problem, widely used as a strong baseline. Which built-in algorithm is the common choice?
- Semantic Segmentation
- XGBoost
- Object2Vec
- DeepAR only
Show answer & explanation
Correct: B — XGBoost. The SageMaker built-in XGBoost algorithm is the standard, high-performing choice for structured/tabular classification and regression and is a common strong baseline — matching the description.
Why not the others: Semantic Segmentation (A) is for pixel-level image tasks; Object2Vec (C) embeds pairs of objects; DeepAR (D) is for time-series forecasting, not general tabular classification/regression.
Source: AWS — SageMaker built-in algorithms →A team wants SageMaker to automatically search combinations of learning rate and other hyperparameters to find the best-performing model. Which capability does this?
- SageMaker Model Monitor
- SageMaker Pipelines
- SageMaker Feature Store
- SageMaker automatic model tuning (hyperparameter optimization)
Show answer & explanation
Correct: D — SageMaker automatic model tuning. Automatic model tuning (hyperparameter optimization) runs many training jobs across a defined hyperparameter search space to find the combination that optimizes a chosen objective metric — exactly the described task.
Why not the others: Model Monitor (A) watches production models; Pipelines (B) orchestrates workflow steps; Feature Store (C) manages features — none performs hyperparameter search.
Source: AWS — SageMaker automatic model tuning →After deploying a model to a SageMaker endpoint, a team wants to automatically detect when incoming data drifts from the training baseline so they know when to retrain. Which capability provides this?
- SageMaker Model Monitor
- SageMaker Data Wrangler
- SageMaker Ground Truth
- SageMaker Autopilot
Show answer & explanation
Correct: A — SageMaker Model Monitor. Model Monitor continuously monitors deployed endpoints, comparing live data and predictions against a baseline to detect data-quality and drift issues and raise alerts — the described post-deployment monitoring need.
Why not the others: Data Wrangler (B) is pre-training data prep; Ground Truth (C) labels data; Autopilot (D) builds models automatically — none monitors a live endpoint for drift.
Source: AWS — SageMaker Model Monitor → Further reading: PowerKram — monitoring ML in production →A team wants to define a repeatable, CI/CD-friendly ML workflow — data processing, training, evaluation, and model registration — natively within SageMaker. Which capability is purpose-built for this?
- A single SageMaker training job run manually
- SageMaker Pipelines
- Amazon QuickSight
- AWS Config rules
Show answer & explanation
Correct: B — SageMaker Pipelines. SageMaker Pipelines is the purpose-built service for creating and automating repeatable, CI/CD-friendly ML workflows — chaining processing, training, evaluation, and model-registry steps — which is exactly the orchestration described.
Why not the others: a single manual training job (A) is not a repeatable orchestrated pipeline; QuickSight (C) is business-intelligence dashboards; Config rules (D) evaluate resource compliance, not ML workflows.
Source: AWS — SageMaker Pipelines → Further reading: PowerKram — MLOps orchestration on AWS →A team needs a serverless way to discover, catalog, and run extract-transform-load (ETL) jobs to prepare data from multiple sources for ML training. Which AWS service is the standard choice?
- Amazon CloudFront
- AWS Shield
- Amazon Cognito
- AWS Glue
Show answer & explanation
Correct: D — AWS Glue. Glue is the serverless data-integration service for discovering, cataloging (Glue Data Catalog), and running ETL jobs across sources — a core data-preparation service for feeding ML training pipelines.
Why not the others: CloudFront (A) is a CDN; Shield (B) is DDoS protection; Cognito (C) handles user authentication — none is a data-integration/ETL service.
Source: AWS — What is AWS Glue? →A team wants to orchestrate a multi-step ML workflow that coordinates several AWS services with branching and retry logic, using a serverless visual state machine. Which service fits?
- Amazon SNS alone
- AWS Batch only
- AWS Step Functions
- Amazon Kinesis Data Firehose
Show answer & explanation
Correct: C — AWS Step Functions. Step Functions is a serverless orchestrator that coordinates multiple AWS services as a visual state machine with built-in branching, retries, and error handling — commonly used alongside SageMaker to orchestrate ML workflows.
Why not the others: SNS (A) is pub/sub messaging; Batch (B) runs batch compute jobs but is not a general multi-service orchestrator with visual state logic; Firehose (D) streams data to destinations, not workflow orchestration.
Source: AWS — What is AWS Step Functions? →A managed platform is needed to build, train, and deploy machine learning models end to end, with notebooks, training jobs, and hosting in one place. Which AWS service is the foundation for ML engineering on AWS?
- Amazon Rekognition
- Amazon Comprehend
- Amazon SageMaker
- Amazon Polly
Show answer & explanation
Correct: C — Amazon SageMaker. SageMaker is AWS’s fully managed platform for the end-to-end ML lifecycle — building (notebooks), training, tuning, deploying, and monitoring models — and is the service the MLA-C01 exam is built around.
Why not the others: Rekognition (A) is a pre-built image/video analysis service; Comprehend (B) is pre-built NLP; Polly (D) is text-to-speech. Those are ready-made AI services, not the platform for building custom models.
Source: AWS — What is Amazon SageMaker? →Keep going: Learning & Career resources
MLA-C01 opens two paths — deeper AWS AI/ML certifications, and production ML engineering roles. Both PowerKram hubs back this exam.
Deep dive: MLA-C01 format, scoring, study path, and how it differs from MLS-C01
Exam format and scoring
MLA-C01 delivers 65 questions in 130 minutes. Only 50 are scored; the other 15 are unscored items AWS uses to trial future questions, and they are not identified during the exam. Alongside the usual multiple-choice and multiple-response items, MLA-C01 introduced newer formats: ordering (arrange 3–5 steps in sequence), matching (pair items across two lists), and case studies (one scenario with several independently scored questions). Scoring is scaled from 100 to 1000 with a passing mark of 720, and it is compensatory: you pass on the overall score. Read the AWS AI/ML services deep dive →
How MLA-C01 differs from Machine Learning – Specialty (MLS-C01)
MLA-C01 is the associate-level, operations-focused successor concept to the MLS-C01 Specialty, which is heading toward retirement. MLS-C01 leaned into ML theory, algorithm selection, and statistical foundations — ideal for data scientists. MLA-C01 is more practical: SageMaker pipelines, feature stores, model registries, MLOps, monitoring, and deployment. If you build and run production ML, MLA-C01 aligns better with the day-to-day work; the two credentials complement rather than duplicate each other. Compare with the MLS-C01 Specialty →
Realistic study path
Most candidates need six to ten weeks depending on hands-on experience. Because the exam is so SageMaker-centric, the highest-return preparation is doing rather than reading: build a small end-to-end project in a SageMaker account — ingest and prepare data (Data Wrangler, Feature Store), train and tune a model (built-in algorithm plus automatic model tuning), deploy to an endpoint, and add Model Monitor. Spend the most time on the 28% Data Preparation domain, then Model Development (26%). Read the SageMaker workflow guide →
Cost, scheduling, and retake policy
The exam costs $150 USD (regional pricing varies) and is delivered at a Pearson VUE test center or online with a proctor. If you do not pass, AWS requires a 14-day wait before you retake, with no annual cap on attempts, and each attempt needs a new registration and fee. If you already hold any AWS certification, a 50% discount voucher is available in your AWS Certification account. AWS’s official MLA-C01 exam guide →
Recertification
AWS certifications are valid for three years. You recertify either by passing the current version of this exam or by earning a higher-level AWS certification before it expires. Pairing MLA-C01 with the data-focused Data Engineer – Associate or the foundational AI Practitioner builds a well-rounded AI/ML profile. See the Data Engineer Associate pairing →
Career outlook for MLA-C01
Machine Learning Engineer – Associate is a strong signal for production ML roles: ML engineer, MLOps engineer, and AI engineer positions that build and operate models on AWS rather than only researching them. It arrived to fill a genuine hiring gap — engineers who can get models into production — and pairs naturally with data-engineering and AI-practitioner credentials. Career Hub — AI Engineer →
AWS Machine Learning Engineer Associate (MLA-C01) exam FAQ
What are the MLA-C01 exam domains and weights?
What is the MLA-C01 passing score?
How many questions is MLA-C01 and how long is it?
Does MLA-C01 have prerequisites, and how is it different from MLS-C01?
How much does MLA-C01 cost and how long is it valid?
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