AWS Certified Machine Learning Associate Exam Topics & Practice Exams

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AWS Certified Machine Learning Engineer – Associate Exam Topics

Exam basics

Format: 50 scored questions and 15 unscored questions
Question types: multiple choice, multiple response, ordering, matching, and case study
Scoring: scaled 100–1000 with a minimum passing score of 720
Scoring model: compensatory across domains
Target candidate: 1+ year with SageMaker plus experience in a related role
Unscored items are included for future exam development

Domain 1: Data preparation for ML (28%)

Ingest and store data from S3, EFS, FSx; stream with Kinesis, Managed Flink, and Kafka
Choose formats such as Parquet, ORC, Avro, JSON, and CSV based on access patterns
Transform and engineer features with Glue, Glue DataBrew, EMR Spark, and SageMaker Data Wrangler
Use SageMaker Feature Store for feature management and Ground Truth for labeling
Validate quality and mitigate bias with Glue Data Quality and SageMaker Clarify
Apply encryption, anonymization, and PII/PHI handling aligned to compliance needs

Domain 2: ML model development (26%)

Select algorithms and services for use cases; consider interpretability and cost
Use SageMaker built-in algorithms and frameworks with script mode for TensorFlow and PyTorch
Fine-tune foundation models via SageMaker JumpStart and Amazon Bedrock
Apply regularization, early stopping, distributed training, and hyperparameter tuning with SageMaker AMT
Evaluate with accuracy, precision, recall, F1, RMSE, ROC/AUC, and confusion matrices
Manage versions and artifacts in SageMaker Model Registry; debug with SageMaker Model Debugger

Domain 3: Deployment & orchestration (22%)

Choose endpoints: real-time, serverless, asynchronous, and batch inference
Select compute for training and inference across CPU, GPU, and instance families
Containerize with ECR and deploy to SageMaker, ECS, EKS, or Lambda; BYOC when needed
Automate with IaC using CloudFormation and CDK; configure VPC access for endpoints
Build CI/CD with CodePipeline, CodeBuild, and CodeDeploy; use blue/green, canary, and linear strategies
Orchestrate workflows with SageMaker Pipelines, EventBridge rules, Step Functions, or MWAA

Domain 4: Monitoring, maintenance, & security (24%)

Monitor data quality, drift, and model performance with SageMaker Model Monitor and Clarify
Track infrastructure with CloudWatch metrics, Logs Insights, X-Ray, and dashboards
Optimize cost with Cost Explorer, Budgets, Trusted Advisor, and Inference Recommender
Secure with IAM roles and policies, KMS encryption, Secrets Manager, VPC isolation, and logging
Use A/B testing and shadow variants to validate model updates in production

Out of scope

Designing full end-to-end ML architectures or setting organization-wide ML strategy
Deep specialization across multiple ML domains such as advanced NLP and computer vision simultaneously
Quantization analysis and advanced numerical tradeoff studies
Integrating a wide array of unrelated services beyond the associate scope

How to prepare

Study the official guide and master the four domains and their task statements
Practice with SageMaker hands-on: Data Wrangler, Feature Store, training jobs, endpoints, and Pipelines
Build CI/CD with CodePipeline, CodeBuild, and CodeDeploy; deploy to SageMaker, ECS, or EKS
Run practice exams to learn question styles and identify weak areas
Instrument models with Model Monitor and Clarify, and track costs with Budgets and Cost Explorer

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