MLA-C01 cheat sheet

Exam at a glance

Domain weightings

How much of the exam each domain covers. Spend your study time in proportion — the heavier the domain, the more questions you'll see.

Key terms

Amazon SageMaker

Amazon SageMaker is AWS's managed platform for building, training, tuning, deploying, and monitoring machine-learning models. It is the central service across every MLA-C01 domain, from data prep to production monitoring.

Feature Engineering

Feature engineering is the process of transforming raw data into input features that improve model performance, including encoding, scaling, and handling missing values. It is a core part of the MLA-C01 data-preparation domain.

Feature Store

Amazon SageMaker Feature Store is a repository for storing, sharing, and serving curated ML features for training and inference consistently. MLA-C01 covers it for reusing features and avoiding training/serving skew.

Hyperparameter Tuning

Hyperparameter tuning is the process of searching for the hyperparameter values that produce the best model performance, which SageMaker automates with automatic model tuning. MLA-C01 covers tuning strategies and avoiding overfitting.

Model Training

Model training is the process of fitting an algorithm to prepared data to learn patterns, run in SageMaker as managed training jobs. MLA-C01 covers selecting algorithms, training at scale, and tracking experiments.

Overfitting

Overfitting is when a model learns the training data too closely, including noise, and generalizes poorly to new data. MLA-C01 covers detecting it (train vs validation metrics) and mitigations such as regularization and more data.

Model Evaluation

Model evaluation is measuring how well a trained model performs using metrics appropriate to the task, such as accuracy, precision/recall, F1, AUC, or RMSE. MLA-C01 requires choosing the right metric and reading a confusion matrix.

Model Registry

The Amazon SageMaker Model Registry is a catalog for versioning models, managing approval status, and promoting them to deployment. MLA-C01 covers it as part of ML lifecycle management and CI/CD.

Model Endpoint

A model endpoint is a deployed model that serves inference requests, which SageMaker offers as real-time, serverless, asynchronous, or batch options. MLA-C01 covers choosing and scaling the right inference option for a workload.

Batch Transform

Batch transform is a SageMaker inference option that runs predictions on a whole dataset at once rather than serving live requests. MLA-C01 contrasts it with real-time endpoints for cost and latency trade-offs.

SageMaker Pipelines

Amazon SageMaker Pipelines is a service for building and automating end-to-end ML workflows — data prep, training, evaluation, and deployment — as a CI/CD pipeline. MLA-C01 covers orchestrating ML workflows with it and Step Functions.

Model Monitor

Amazon SageMaker Model Monitor detects data quality issues, drift, and bias in deployed models by comparing live traffic against a baseline. MLA-C01 covers it for the monitoring and maintenance domain.

Data Drift

Data drift is a change in the statistical distribution of production input data compared to training data, which can degrade model accuracy over time. MLA-C01 covers detecting drift with Model Monitor and triggering retraining.