Bias Detection and Mitigation in SageMaker Data Wrangler

• A.Amazon SageMaker Model Monitor
• B.Amazon SageMaker Debugger
• ✓ C.Amazon SageMaker Clarify
• D.Amazon SageMaker Pipelines

Why C: Amazon SageMaker Clarify is the correct feature because it is specifically designed to detect bias in datasets and machine learning models. It provides built-in bias metrics (e.g., pre-training bias) and can generate bias reports during data preparation, directly addressing the need to identify potential bias before training.

• A.Visual data profiling
• B.Built-in transforms for missing values
• C.Export to Feature Store
• ✓ D.Bias detection with Amazon SageMaker Clarify

Why D: Data Wrangler integrates with Amazon SageMaker Clarify for bias detection. Therefore, the correct answer is D: Bias detection with Amazon SageMaker Clarify. Options A and B are features of Data Wrangler but not specifically for bias detection. Option C (Export to Feature Store) is unrelated to bias detection.

• A.SageMaker Debugger
• B.SageMaker Experiments
• C.SageMaker Model Monitor
• ✓ D.SageMaker Clarify

Why D: Amazon SageMaker Clarify provides bias detection capabilities, including pre-training bias metrics, and can be integrated with Data Wrangler to analyze datasets. Data Wrangler itself does not have built-in bias detection, but it can export to Clarify for analysis.

• A.Apply MinMaxScaler to the target variable
• ✓ B.Use SageMaker Clarify to evaluate bias in the dataset
• C.Apply one-hot encoding to all categorical features
• D.Remove all features with a correlation above 0.8

Why B: Option B is correct because SageMaker Clarify is specifically designed to detect various types of bias in datasets and models, including regression bias. Even after standard scaling, the target variable's distribution (mean=50, std=20) may still contain systemic biases related to sensitive attributes (e.g., race, gender). SageMaker Clarify computes bias metrics such as Conditional Demographic Disparity in Labels (CDDL) and can identify whether the model's predictions are unfairly skewed across demographic groups, which is the direct step needed to reduce model bias.

• ✓ A.Integration with Amazon SageMaker Clarify for bias reports
• B.Built-in transform for SMOTE oversampling
• C.Use of Amazon Athena to query data for bias patterns
• D.Export to Amazon SageMaker Feature Store

Why A: Amazon SageMaker Data Wrangler integrates directly with Amazon SageMaker Clarify to detect bias in datasets. This integration allows you to run bias analysis on your data before training, generating reports that highlight potential imbalances or unfairness in features and target variables. It is the correct capability for the team's stated need.