MLA-C01 Flashcards — Free AWS Certified Machine Learning Engineer Associate MLA-C01 Study Cards

Use Retrieval-Augmented Generation (RAG) with the policy documents indexed in a vector store

RAG allows the LLM to retrieve relevant document sections at inference time, so knowledge stays current without retraining.

validation:auc

For binary classification, AUC (Area Under the ROC Curve) is the standard evaluation metric because it measures the model's ability to discriminate between the two classes across all classification thresholds, independent of class balance. SageMaker's built-in XGBoost exposes validation:auc as the objective metric for binary classification tuning. MAE and RMSE are regression metrics, and NDCG is a ranking metric, so none of them fit binary classification.

Explainability report generation

SageMaker Autopilot's explainability report generation feature produces SHAP-based feature importance values that quantify each input feature's global contribution to model predictions. Enabling it during the AutoML job causes Autopilot to emit a model explainability report alongside the best candidate, satisfying the requirement for a global feature-importance view. The other options relate to candidate exploration, model combination, or tuning, none of which produce explainability artifacts.

Use the SageMaker PyTorch estimator with a requirements.txt file

The SageMaker PyTorch estimator supports a 'requirements.txt' file in the source directory (specified via 'source_dir'), which SageMaker automatically installs into the training container before the script runs. This is the simplest, AWS-recommended way to add Python packages without building a custom image. It preserves the managed PyTorch container's optimizations while adding only the extra dependencies.

Use Amazon SageMaker Data Wrangler to impute missing values using built-in transforms.

Amazon SageMaker Data Wrangler provides a visual interface and built-in transforms for handling missing values efficiently at scale, without writing custom code. Glue ETL is more code-heavy, and imputation with pandas is not scalable for large datasets. Removing all rows with missing values is not always optimal and may not be efficient.

Remove the column from the feature set.

'CustomerID' is a unique identifier with no predictive power for churn. Including it as a feature would cause the model to memorize individual customers rather than learn generalizable patterns, leading to overfitting and poor performance on unseen data. In machine learning, such columns should be removed during data preparation to ensure the model learns from meaningful features.

Parse date transform.

The 'Parse date' transform in Amazon SageMaker Data Wrangler is specifically designed to convert date strings into structured datetime components. By applying this transform to the 'YYYY-MM-DD' column, the data scientist can automatically extract year, month, and day as separate features, enabling downstream feature engineering without manual string parsing.

Log transformation (natural log)

A log transformation is appropriate for heavily right-skewed data because it compresses the long tail by applying a concave function, pulling extreme values closer to the mean and making the distribution more symmetric. In AWS Glue ETL, you can apply this using Spark SQL's `LOG` function or a Python UDF with `numpy.log`, which directly addresses the skewness to better approximate a Gaussian distribution for downstream ML models.

Read the Parquet files directly using SparkSession.read.parquet

SageMaker Processing natively integrates with Apache Spark, and reading Parquet files directly via `SparkSession.read.parquet` leverages columnar storage, predicate pushdown, and compression (e.g., Snappy) to minimize I/O and deserialization overhead. This approach is far more efficient than text-based or format-conversion methods, as Parquet is optimized for analytical workloads and preserves schema information.

Real-time endpoint on ml.g4dn instances

Real-time endpoints on GPU instances (ml.g4dn) provide low latency and GPU acceleration, ideal for high-traffic, latency-sensitive workloads.

Use a single multi-model endpoint (MME)

A single multi-model endpoint (MME) hosts many models behind one HTTPS endpoint and loads them on demand into shared compute, which is ideal for 200 infrequently used small models because you pay for one endpoint's underlying instances rather than 200 separate ones. SageMaker dynamically loads and unloads models from S3 as requests arrive, dramatically reducing hosting cost while still providing real-time HTTPS inference.

Pipeline caching

SageMaker Pipelines caching stores the output of a step keyed by the step's inputs (code, data, hyperparameters, etc.). When a pipeline run executes and the inputs are unchanged, the cached output is reused and the step is skipped, avoiding redundant training. This directly addresses the requirement to skip training when input data hasn't changed.

Data quality monitoring

Data quality monitoring in SageMaker Model Monitor compares the statistical distribution of each input feature (both numerical and categorical) against a baseline computed from the training data, detecting drift in feature distributions over time. It supports categorical and numerical columns and is the correct feature for detecting per-feature distribution changes.

OverheadLatency

OverheadLatency measures the time taken by the SageMaker infrastructure to handle requests before and after model inference, including request routing, authentication, and response processing. Since ModelLatency is stable but total endpoint latency has increased, the extra time must be in the overhead component, making OverheadLatency the correct metric to investigate.

SageMaker ML Lineage Tracking

SageMaker ML Lineage Tracking creates a graph of artifacts (datasets, models) and actions (training jobs, endpoints) to track the provenance of ML workflows.

Target tracking scaling

Target tracking scaling is correct because it adjusts capacity to keep a specified metric, such as CPU utilization, at a target value. Application Auto Scaling for SageMaker endpoints supports target tracking, which automatically creates and manages the necessary CloudWatch alarms and scaling policies. This is the recommended approach for maintaining a utilization target without manually defining step adjustments.