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AWS Machine Learning Associate exam topics, tips and practice exams

Cameron McKenzie
Cameron McKenzie
TechTarget

AWS Machine Learning Associate Book for Certification

The AWS Machine Learning Associate exam validates real-world ability to build, operationalize, deploy, and maintain ml solutions on aws. You will prepare data pipelines, select models, train and tune efficiently, choose the right hosting pattern, automate with CI/CD, monitor quality and drift, and secure your stack. Typical candidates have about a year working with amazon sagemaker and adjacent services in roles like backend developer, data engineer, data scientist, or devops.

Exam basics

You will encounter multiple-choice, multiple-response, ordering, matching, and case-study items. Scores are reported from 100–1,000 with a passing score of 720. AWS uses a compensatory model, so you pass on overall performance rather than by section. Expect a few unscored items that AWS is trialing for future forms.

If you are building a longer certification roadmap, compare the scoring profile and depth here to paths like the solutions architect, the hands-on developer associate, the data-heavy data engineer associate, and even cross-cloud options such as the gcp machine learning engineer or gcp generative ai leader.

Content domains and weights

Domain 1: data preparation for machine learning

Master data formats and pipelines that feed ml workloads. Lean on services you will see repeatedly across exams, including amazon sagemaker data wrangler, sagemaker feature store, amazon s3, amazon emr, and streaming with amazon kinesis.

Ingest and store data

Choose between parquet, orc, avro, json, and csv. Land and read data from s3, efs, fsx, rds, and dynamodb. For streams, use kinesis or managed flink. Make initial storage decisions on cost, latency, and access patterns.

Transform and engineer features

Clean, impute, deduplicate, scale, standardize, bin, and tokenize with aws glue, glue databrew, emr (spark), or sagemaker data wrangler. Register reusable features in feature store and label with sagemaker ground truth.

Ensure data integrity

Validate quality with glue data quality, address class imbalance, anonymize pii/phi, and encrypt with keys managed by security tooling covered across exams like aws kms. Configure high-throughput reads via efs or fsx for training clusters.

Domain 2: ml model development

Translate business goals into algorithms or managed ai services. Know when to use sagemaker built-in algorithms, when to fine-tune a foundation model with sagemaker jumpstart or amazon bedrock, and when to call task-specific services like rekognition, transcribe, translate, and comprehend.

Train and refine

Use frameworks in script mode (tensorflow, pytorch), reduce training time with distributed strategies and early stopping, and tune systematically with sagemaker automatic model tuning. Manage versions in the sagemaker model registry and combine learners with ensembling or boosting.

Analyze performance

Pick metrics that fit the task: accuracy, precision/recall, f1, rmse, roc, and auc. Inspect bias and interpretability using sagemaker clarify, and debug convergence issues with sagemaker debugger. Validate changes via shadow or a/b deployments.

Domain 3: deployment and orchestration of ml workflows

Select the right hosting pattern on sagemaker: real-time endpoints, asynchronous inference, batch transform, or serverless. Balance cost and latency, and consider multi-model or multi-container endpoints. For edge, optimize with sagemaker neo.

Create and script infrastructure

Automate with aws cloudformation or the aws cdk. Containerize with amazon ecr, amazon ecs, or amazon eks, and bring your own container to sagemaker for custom runtimes. Configure endpoint auto scaling on model latency, invocations per instance, or cpu utilization.

Build cicd for ml

Orchestrate pipelines with aws codepipeline, codebuild, and codedeploy, or use sagemaker pipelines plus amazon eventbridge and aws step functions. Add unit, integration, and end-to-end tests and wire in automated retraining triggers.

Domain 4: ml solution monitoring, maintenance, and security

Monitor inference quality

Detect data and concept drift with sagemaker model monitor, track fairness with clarify, and validate changes through safe rollout patterns. These practices echo reliability themes you will also see on the solutions architect professional and devops exams.

Observe infrastructure and costs

Instrument with amazon cloudwatch metrics, logs, dashboards, logs insights; trace with aws x-ray; audit with aws cloudtrail. Right-size using sagemaker inference recommender and manage spend with aws budgets and cost explorer. Consider on-demand, spot, reserved, or sagemaker savings plans.

Secure aws resources

Apply least privilege with aws iam, encrypt with aws kms, isolate networks in amazon vpc, and secure cicd artifacts. Monitor posture using services also featured across security certifications.

Out of scope tasks

You are not expected to architect enterprise-wide ml programs, define org-level strategy, integrate broadly across every toolchain, become a deep expert in multiple ml sub-disciplines at once, or perform detailed model quantization analysis. The focus is practical ml engineering on aws.

How to prepare

  1. Start with the blueprint: map every study task to a domain objective from this guide and compare with adjacent paths like ai practitioner or ml specialty.
  2. Practice early: take a baseline set of questions to learn how aws exam items are phrased and expose blind spots.
  3. Build hands-on: run an end-to-end project in sagemaker studio using data wrangler, feature store, automatic model tuning, and model monitor.
  4. Automate workflows: stitch together codepipeline, codebuild, codedeploy, and sagemaker pipelines for train-evaluate-register-deploy.
  5. Drill with mocks: schedule timed exams, then document why the right choice meets the requirement and why each distractor fails.
  6. Tune your instincts: watch for cues like “lowest latency,” “most cost-effective,” “least operational effort,” or “meet compliance,” and map them to the best architectural choice.

Where to go next?

After mla-c01, consider deepening skills with ml specialty, broadening architecture with the solutions architect associate and professional, or rounding out platform skills with devops engineer professional. If you operate in poly-cloud environments, explore complementary tracks on gcp such as the ml engineer or solutions architect professional.

Cameron McKenzie Cameron McKenzie is an AWS Certified AI Practitioner, Machine Learning Engineer, Solutions Architect and author of many popular books in the software development and Cloud Computing space. His growing YouTube channel training devs in Java, Spring, AI and ML has well over 30,000 subscribers.

Next Steps

So what’s next?

A great way to secure your employment or even open the door to new opportunities is to get certified.

If you’re interested in AWS products, here are a few great resources to help you get Cloud Practitioner, Solution Architect, Machine Learning and DevOps certified from AWS:

Put your career on overdrive and get AWS certified today!