AssociateAWS
AWS Certified Machine Learning Engineer – Associate — Study Guide & Exam Overview
ML engineering on AWS is one of the fastest-growing specializations in tech. This certification demonstrates you can take a model from data preparation to production deployment and keep it reliable — skills that bridge the gap between data science and cloud engineering.
Duration
170 min
Passing Score
720 / 1000 points
Level
Associate
Provider
AWS
What This Certification Covers
- End-to-end ML workflows with SageMaker: Studio, training jobs, tuning, and deployment
- Feature engineering, data preprocessing, and model evaluation best practices
- Model deployment patterns: real-time endpoints, batch transform, multi-model, and serverless
- MLOps: model monitoring, drift detection, CI/CD for ML, and A/B testing
- Security and governance for ML workloads: IAM roles, VPC isolation, and encryption
Exam Domain Breakdown
- Data Preparation (28%)
- Model Development (26%)
- Deployment & Orchestration (22%)
- Monitoring & Security (24%)
Exam Format
65 multiple-choice and multiple-response questions, 170 minutes, scored 100–1000 with passing score of 720
Study Tips to Pass ML Engineer
- 1Get hands-on with SageMaker Studio — the exam heavily tests practical knowledge of the platform
- 2Understand SageMaker Pipelines for end-to-end ML automation and reproducibility
- 3Study Model Monitor for detecting data drift and concept drift in production models
- 4Know when to use SageMaker JumpStart, Autopilot, and Canvas for low-code ML
- 5Review SageMaker Feature Store and the role of feature groups in training and inference
Ready to test your knowledge? Take a free timed mock exam below.
Official ML Engineer exam page