A company is preparing data to train a new ML model on Amazon SageMaker AI. The data has not been used before for ML training. The data includes duplicates and is missing some values. The company…

Question

Options

• AUse SageMaker Clarify to create data quality rules. Use SageMaker Model Monitor to detect bias.
• BUse SageMaker Data Wrangler to create data quality rules. Use SageMaker Clarify to detect bias.
• CUse SageMaker Debugger to create data quality rules. Use SageMaker Model Monitor to detect
• DUse SageMaker Model Monitor to create data quality rules. Use SageMaker Clarify to detect bias.

• A
  4% (2)
• B
  82% (41)
• C
  4% (2)
• D
  10% (5)

Explanation

SageMaker Data Wrangler is purpose-built for pre-training data preparation: it can profile data quality, remove duplicates, impute missing values, and apply transformations-all through a visual interface. SageMaker Clarify is purpose-built for detecting statistical bias in datasets and model predictions. Together, they cover both requirements: Data Wrangler for data quality and Clarify for bias detection. Option A reverses the tools incorrectly-Clarify does not create data quality rules, and Model Monitor is for monitoring deployed models, not raw datasets. Option C uses SageMaker Debugger, which monitors training job internals (gradients, weights), not dataset quality. Option D misapplies Model Monitor, which is designed for post-deployment drift detection, not pre-training data cleaning.

No community discussion yet for this question.