AWS Services for SageMaker Model Data Drift and Quality Monitoring

• ✓ A.Create a baseline from the training data to compare against live data.
• B.Use SageMaker Debugger to analyze training jobs.
• C.Set up an AWS Lambda function to preprocess incoming requests.
• D.Configure Amazon S3 bucket notifications for model artifacts.
• ✓ E.Enable data capture on the SageMaker endpoint.

Why A: Option A is correct because creating a baseline from the training data establishes a statistical profile (e.g., mean, standard deviation, distribution) of expected input features. SageMaker Model Monitor then compares live endpoint data against this baseline to detect data quality drift, such as missing values or feature distribution shifts. Option E is correct because enabling data capture on the SageMaker endpoint is a prerequisite for Model Monitor to collect and analyze the live inference data. Without data capture, there is no live data to compare against the baseline, making monitoring impossible. Therefore, both actions are required together for effective data quality monitoring.

• A.SageMaker Debugger and Amazon SNS
• B.SageMaker Pipelines and AWS Lambda
• C.SageMaker Clarify and AWS Config
• ✓ D.SageMaker Model Monitor and Amazon CloudWatch

Why D: SageMaker Model Monitor is the native SageMaker feature designed specifically for continuously monitoring deployed models for data drift, bias drift, and feature attribution drift. It automatically captures inference requests and responses, computes statistics, and publishes metrics to Amazon CloudWatch, which can trigger alarms for drift detection. This combination provides automated drift detection with minimal operational overhead because it requires no custom infrastructure or manual scheduling.

• A.Set up Amazon CloudWatch alarms on the endpoint's invocation latency metric.
• ✓ B.Enable data capture on the SageMaker endpoint to store inference data in Amazon S3.
• ✓ C.Configure Amazon SageMaker Model Monitor to run hourly monitoring schedules.
• D.Deploy a shadow endpoint to compare predictions from the current model and a challenger model.
• E.Create a baseline from the training data to serve as a reference distribution.

Why B: Option B is correct because enabling data capture on the SageMaker endpoint is essential to store the actual inference requests and responses in Amazon S3. Without this captured data, there is no production data to compare against the baseline for drift detection. Option C is correct because SageMaker Model Monitor must be configured with a monitoring schedule (e.g., hourly) to automatically run statistical tests comparing the captured inference data against the baseline distribution, triggering alerts when drift is detected.