Why This Cheat Sheet Matters for MLA-C01
This cheat sheet covers the most important ML Model Training concepts tested on the MLA-C01 (AWS Machine Learning Engineer Associate) certification exam. It contains 2 sections with 8 key points that you should memorize before exam day. Study supervised and unsupervised training, SageMaker built-in algorithms, distributed training, model artifacts, and training job configuration. Use this as a quick-reference guide during your final review sessions.
2Sections
8Key Points
Training Decisions
- Classification predicts categories; regression predicts continuous values.
- Clustering finds groups without labels; anomaly detection finds unusual patterns.
- Forecasting predicts future time-series values.
- Distributed training helps with large datasets or models that exceed single-instance limits.
Metrics
- Accuracy can be misleading with imbalanced classes.
- Precision answers: of predicted positives, how many were correct?
- Recall answers: of actual positives, how many were found?
- RMSE and MAE are common regression metrics.
Practice Model Training Questions
Put your knowledge to the test with practice questions.