A machine learning (ML) specialist uploads 5 TB of data to an Amazon SageMaker Studio environment. The ML specialist performs initial data cleansing. Before the ML specialist begins to train a model,

Question

Options

• AUse SageMaker Clarify to automatically detect data bias
• BTurn on the bias detection option in SageMaker Ground Truth to automatically analyze data
• CUse SageMaker Model Monitor to generate a bias drift report.
• DConfigure SageMaker Data Wrangler to generate a bias report.
• EUse SageMaker Experiments to perform a data check

• A
  79% (22)
• B
  7% (2)
• C
  11% (3)
• E
  4% (1)

Explanation

SageMaker Clarify (A) is the dedicated service for detecting bias in datasets and models. It analyzes statistical properties of data to surface potential bias across sensitive features before training begins. SageMaker Data Wrangler (D) has a native 'Generate data bias report' feature that integrates directly with Clarify under the hood, allowing the ML specialist to produce a visual bias report without leaving the Studio environment. Together, they provide the least-overhead path: Data Wrangler handles the report generation UI, and Clarify performs the analysis. Option B (Ground Truth) is for data labeling, not bias detection. Option C (Model Monitor) detects bias drift in deployed models post-training. Option E (Experiments) tracks training runs and metrics, not dataset bias.

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