MLA-C01 replaced the old ML Specialty exam's role as AWS's flagship machine learning credential, but it asks almost nothing about picking the "best" algorithm from a textbook list. Most questions hand you a working pipeline that is already broken somewhere: a SageMaker endpoint that is too slow, a training job that keeps failing on a Spot interruption, a feature pipeline producing training and serving skew. You are graded on operating a system, not selecting a model. That framing catches a lot of candidates off guard, especially ones who studied ML theory expecting a data science exam.
AWS structures MLA-C01 around four domains, and the split is unusually flat: no single domain dominates, and none can be safely skipped.
Data Preparation for Machine Learning (28%) — ingesting data from S3, Redshift, and RDS; cleaning, deduplicating, and handling missing or imbalanced data; feature engineering with SageMaker Data Wrangler and Feature Store; understanding data formats (Parquet, RecordIO, CSV) and their tradeoffs for training throughput.
ML Model Development (26%) — choosing between built in SageMaker algorithms, custom containers, and Bedrock foundation models; hyperparameter tuning with SageMaker Automatic Model Tuning; evaluating models with the right metric for the problem (F1 versus AUC versus RMSE); versioning models in the Model Registry.
Deployment and Orchestration of ML Workflows (22%) — time versus batch versus asynchronous inference endpoints, multi model and multi container endpoints, auto scaling policies, and stitching it all together with SageMaker Pipelines, Step Functions, or Airflow (MWAA).
ML Solution Monitoring, Maintenance, and Security (24%) — Model Monitor for drift detection, CloudWatch and CloudTrail for operational visibility, IAM roles and VPC configuration for SageMaker resources, encryption of data at rest and in transit, and cost optimization (Spot training, right sized instances, serverless inference).
Recent revisions to the exam guide have also folded in generative AI content: Bedrock model selection and customization, retrieval augmented generation patterns, and responsible AI practices sit alongside the classic supervised and unsupervised learning material. A candidate studying only from a 2023 era ML Specialty guide will find gaps here.
The candidate pool splits into two groups. The first is practicing ML engineers and MLOps engineers already running SageMaker in production who want the credential to match the job they're doing. The second, larger group is cloud engineers and data engineers moving toward ML roles, often coming from the AWS Certified Data Engineer Associate or AWS Certified Developer Associate credentials, who need this exam to formalize a lateral move into ML infrastructure work. AWS's own recommended background, one to two years of hands on ML implementation on AWS plus familiarity with Python, reflects that second group more than it does research scientists, since the exam has almost no interest in ML theory divorced from AWS tooling.
The official MLA-C01 exam runs 65 questions in 130 minutes, scored on a scale of 100 to 1000 with a passing score of 720. That scaled score means you cannot back calculate "how many can I miss," since unscored pretest questions are mixed in and weighting isn't public. Budget roughly two minutes per question and move on quickly from anything you're stuck on; there's no penalty for guessing.
Beyond standard multiple choice and multiple response, AWS has introduced ordering and matching item types and occasional case study style question sets across its newer associate exams, and MLA-C01 uses these too. A matching question might ask you to pair five SageMaker features with the problem each one solves; an ordering question might ask you to sequence steps in a training pipeline. These formats punish memorized facts without contextual understanding, because you can't pattern match your way through a sequence you don't actually understand.
Where candidates commonly lose points: confusing SageMaker's built in algorithms with when a custom container is actually required, mixing up time endpoints with serverless inference limits (serverless has a maximum payload size and cold start behavior that trips people up), and misjudging which CloudWatch metric actually signals model drift versus infrastructure load.
Because MLA-C01 is still a relatively new exam, the pool of retired questions circulating online is thin compared to older certifications like Solutions Architect Associate. Treat these practice questions as scenario training rather than as coverage of exact exam content: work through each explanation until you understand why the wrong answers are wrong, not just which choice is correct. If you get a data preparation question wrong, go open the actual SageMaker Data Wrangler console and try the transformation yourself rather than re-reading the explanation twice. This exam rewards people who've touched the services, and practice questions are best used to reveal which services you haven't touched yet.
Run timed sets of 15 to 20 questions rather than the full 65 at once early in your prep, since the goal at that stage is finding gaps, not simulating exam day. Save full length timed runs for the final week.
A recurring one is treating this as a rebadged ML Specialty exam and under preparing for the deployment and monitoring domains, which together make up 46% of scored content, more than model development itself. Another is skipping cost and security details because they feel like "DevOps trivia" rather than ML content; AWS explicitly tests IAM roles for SageMaker execution, VPC endpoint configuration for training jobs, and encryption settings, and candidates who focus purely on model accuracy metrics miss these questions. A third is underestimating how much the exam has shifted toward generative AI and Bedrock since its 2025 launch; older study material written before mid-2025 revisions may not reflect the current guide For additional practice, take our free SAA-C03 practice exam .
Build a small end to end project rather than studying domains in isolation: ingest a dataset with Data Wrangler, train with a built in algorithm and a custom container version of the same model, register both in Model Registry, deploy one as time and one as serverless, then set up Model Monitor on the time endpoint. That single project touches all four domains and will surface the operational details multiple choice questions probe for.
Read the actual AWS exam guide PDF before you start studying rather than after, since the four domains map to specific task statements AWS publishes, and vague third party study guides sometimes drift from AWS's own wording. When comparing services, build your own table of "when would I use X over Y" for the pairs the exam likes to test: time versus serverless versus batch transform endpoints, SageMaker's built in XGBoost versus Bedrock, and Step Functions versus SageMaker Pipelines for orchestration.
Work through the practice questions below in short timed sets, log every question you miss along with which domain it falls under, and revisit the AWS service you got wrong in the actual console before moving to the next set. Once you can clear practice sets above 80% consistently across all four domains, not just your strongest one, you're in reasonable shape to schedule the exam through Pearson VUE.
Last updated on Oct, 9 2026
