AWS Certified Machine Learning Engineer – AssociateStudy guide
The associate certification for machine learning engineering on AWS (MLA-C01).
About AWS Certified Machine Learning Engineer – Associate (MLA-C01)
AWS Certified Machine Learning Engineer – Associate (MLA-C01) is a Associate-level certification from AWS. This page organizes the exam scope into a 4-chapter, 11-section study guide and lets you check your understanding with exam-style practice questions. A good flow is to read the chapters below in order, then test yourself via "Practice questions."
Exam domains (approximate weighting)
- Data Preparation for ML~28%
- ML Model Development~26%
- Deployment and Orchestration of ML Workflows~22%
- ML Solution Monitoring, Maintenance, and Security~24%
Weights are approximate guidance for the live exam. Each domain is covered in detail in the chapters and sections below.
Official exam information: https://aws.amazon.com/certification/certified-machine-learning-engineer-associate/
1Data Preparation for ML
- 1.1Data Preparation and Feature Engineering for ML
Understand data preparation for ML (cleansing, handling missing/outliers, encoding, scaling) and feature engineering basics. The starting point for "Data Preparation for ML" in MLA-C01.
- 1.2SageMaker Data-Preparation Tools
Understand SageMaker Data Wrangler (visual prep), Feature Store (store/share features), and large-scale transforms with Processing jobs/Glue.
- 1.3Data Splitting, Quality, and Bias
Understand train/validation/test splitting and preventing data leakage, detecting class imbalance and bias (SageMaker Clarify), and checking data quality.
2ML Model Development
- 2.1Model Selection and Training
Understand three model-building approaches on SageMaker (built-in algorithms, script mode/bring-your-own container, AutoML/JumpStart) and training-job basics. The starting point for "ML Model Development" in MLA-C01.
- 2.2Hyperparameter Tuning and Overfitting
Understand hyperparameter search with SageMaker Automatic Model Tuning, overfitting vs. underfitting, and remedies like regularization and adding data.
- 2.3Model Evaluation Metrics
Understand evaluation metrics for classification (accuracy, precision/recall, F1, AUC) and regression (RMSE, MAE, R-squared), the confusion matrix, and metric choice under imbalance.
3Deployment and Orchestration of ML Workflows
- 3.1Choosing Inference Options
Understand SageMaker inference options (real-time, serverless, asynchronous, batch transform) and choosing by traffic/latency. The starting point for "Deployment and Orchestration" in MLA-C01.
- 3.2MLOps and SageMaker Pipelines
Understand MLOps: automating the ML lifecycle with SageMaker Pipelines, versioning/approval with the Model Registry, and CI/CD integration.
- 3.3Safe Model Deployment Strategies
Understand low-risk model deployment: blue/green, canary/linear traffic shifting, and shadow/A-B testing (multi-variant endpoints).
4ML Solution Monitoring, Maintenance, and Security
- 4.1Model Monitoring and Drift
Understand drift detection with SageMaker Model Monitor, endpoint monitoring with CloudWatch, and retraining triggered by drift. The starting point for "Monitoring, Maintenance, and Security" in MLA-C01.
- 4.2ML Security and Cost
Understand security/cost of ML operations: least privilege with IAM, encryption (KMS/TLS) and VPC isolation, and cost optimization (right-sizing, managed spot training).