Certification guide
AWS Certified Machine Learning Engineer – Associate (MLA-C01) is Amazon’s associate-level credential for the people who build production ML systems on AWS — not the researchers who invent new architectures, but the engineers who ingest and prepare data, train and version models, deploy them, and keep them healthy after they meet real traffic. It is the natural next credential after AWS Cloud Practitioner or Solutions Architect Associate for an engineer moving into MLOps, and the practical counterpart to the AWS Certified AI Practitioner (which is conceptual and vendor-agnostic in tone) and the older Machine Learning Specialty (which leans deeper into modeling theory).
The exam launched in October 2024 and reached general availability in early 2025. Its center of gravity is Amazon SageMaker — Studio, Pipelines, Feature Store, Model Registry, Clarify, Model Monitor, JumpStart, and HyperPod — alongside the surrounding services an ML engineer actually touches: S3 and Glue for data, Bedrock for foundation models, EventBridge and Step Functions for orchestration, KMS and VPC endpoints for security, and CloudWatch for operations. The 2026 refresh reflects the maturing MLOps toolchain and the reality that most exam questions are scenario-based rather than trivia.
Exam code · MLA-C01 65 questions 130 minutes 720 / 1000 scaled · pass $150 USD Valid 3 years
The exam is organized into four content domains. Data preparation is the largest, model development next, then monitoring/maintenance/security, then deployment and orchestration — the ordering is a fair map of where SageMaker engineers spend their time in practice.
Data Preparation for Machine Learning (ML)
Domain 1 · 28%Ingest and store data on S3, Glue (crawlers, ETL, DataBrew, Data Catalog), Athena, Lake Formation, and Kinesis. Transform and engineer features — encoding, scaling, imputation, splits, target derivation — while avoiding leakage. Ensure data integrity: schema enforcement with Glue Schema Registry, quality checks, labeling with SageMaker Ground Truth, and PII minimization patterns. The largest single domain and the one most often underestimated by candidates who focus first on modeling.
ML Model Development
Domain 2 · 26%Choose the right modeling approach — SageMaker built-ins, script mode, BYOC, or JumpStart foundation models. Train with SageMaker training jobs, distributed training libraries, and HyperPod for large runs. Tune hyperparameters with Bayesian, Random, Grid, and Hyperband strategies. Evaluate with metrics appropriate to the task, and track experiments with SageMaker Experiments, Model Registry, and Clarify.
Deployment and Orchestration of ML Workflows
Domain 3 · 22%Match the deployment mode to the workload — real-time, serverless, asynchronous, batch transform, multi-model, or edge (Neo plus IoT Greengrass). Configure autoscaling, canary and blue-green rollouts with deployment guardrails, and CI/CD with SageMaker Pipelines, EventBridge, Step Functions, CodePipeline, and Model Registry approvals. Package and version containers in ECR.
ML Solution Monitoring, Maintenance, and Security
Domain 4 · 24%Watch models in production with the four Model Monitor jobs — data quality, model quality, bias drift, and feature attribution drift — and surface metrics through CloudWatch. Secure ML systems with IAM execution roles, KMS encryption, VPC mode with interface and gateway endpoints, Secrets Manager, and Bedrock Guardrails for foundation-model workloads. Track cost, resilience, and long-retention compliance requirements. Roughly a quarter of the exam.
Each Certifym practice exam is a full 65-question set weighted to the current MLA-C01 blueprint (18 / 17 / 14 / 16 across the four domains), timed at 130 minutes, with a 72 percent raw pass mark — the honest raw-score equivalent of AWS’s 720 / 1000 scaled cut. That is a genuinely tight cut on this exam: it means you cannot coast on the easy domains and get lucky on the heavy ones, and it means the distractor quality on our questions matters more than the sheer count of items you can grind through. Every explanation names the closest wrong answer and says why the winner beats it, so you build the practitioner-level judgment the case-study questions actually test.
AWS Machine Learning Engineer - Associate Practice Exam
Certifym practice bank for AWS Certified Machine Learning Engineer - Associate (MLA-C01). Vertical scenario slant: generic enterprise. Full-length 65-question set weighted to the current MLA-C01 blueprint.…
Subscribe to startFrequently asked questions about MLA-C01
What is the AWS Certified Machine Learning Engineer – Associate (MLA-C01) exam?
MLA-C01 is Amazon’s associate-level credential for the people who build production ML systems on AWS — not the researchers who invent new architectures, but the engineers who ingest and prepare data, train and version models, deploy them, and keep them healthy after they meet real traffic. The exam launched in October 2024 and reached general availability in early 2025.
How many questions are on the MLA-C01 exam and how long is it?
The exam is 65 questions in 130 minutes. Most questions are scenario-based rather than trivia, describing a real ML engineering situation and asking which approach fits best.
What is the passing score for MLA-C01?
AWS reports a scaled score with 720 out of 1000 as the passing mark. Certifym sets the pass mark on each practice set at 72 percent raw, the honest raw-score equivalent of the 720 / 1000 scaled cut. It is a genuinely tight cut: you cannot coast on the easy domains and get lucky on the heavy ones.
How much does the MLA-C01 exam cost?
The exam costs $150 USD.
How long is the MLA-C01 certification valid?
The credential is valid for three years.
What experience should I have before taking MLA-C01?
It is the natural next credential after AWS Cloud Practitioner or Solutions Architect Associate for an engineer moving into MLOps. The exam assumes you already do the work it describes — preparing data, training and versioning models, deploying them, and operating them under real traffic — rather than studying modeling theory in the abstract.
What domains does MLA-C01 cover and how are they weighted?
Four domains: Data Preparation for Machine Learning (28%), ML Model Development (26%), ML Solution Monitoring, Maintenance, and Security (24%), and Deployment and Orchestration of ML Workflows (22%). Data preparation is the largest domain and the one most often underestimated by candidates who focus first on modeling.
Which AWS services does the exam focus on?
Its center of gravity is Amazon SageMaker — Studio, Pipelines, Feature Store, Model Registry, Clarify, Model Monitor, JumpStart, and HyperPod — alongside the surrounding services an ML engineer actually touches: S3 and Glue for data, Bedrock for foundation models, EventBridge and Step Functions for orchestration, KMS and VPC endpoints for security, and CloudWatch for operations.
How does MLA-C01 compare with the AWS Certified AI Practitioner?
MLA-C01 is the practical counterpart to the AWS Certified AI Practitioner, which is conceptual and vendor-agnostic in tone. It also differs from the older Machine Learning Specialty, which leans deeper into modeling theory; MLA-C01 is aimed squarely at the engineer shipping and operating ML systems.
How should I prepare for MLA-C01?
Work through blueprint-weighted practice under real timing. Each Certifym practice exam is a full 65-question set weighted to the current MLA-C01 blueprint (18 / 17 / 14 / 16 across the four domains), timed at 130 minutes, with a 72 percent raw pass mark. Every explanation names the closest wrong answer and says why the winner beats it, so you build the practitioner-level judgment the case-study questions actually test.
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All questions, answers, and explanations on Certifym are original content created for practice purposes. They are not actual AWS examination questions and are not represented as such. Practicing with these materials does not guarantee a passing result on any live certification exam.