AIF-C01 Question #119: Real Exam Question with Answer & Explanation

Question

A company is developing an ML model to make loan approvals. The company must implement a solution to detect bias in the model. The company must also be able to explain the model's predictions. Which solution will meet these requirements?

Options

• AAmazon SageMaker Clarify
• BAmazon SageMaker Data Wrangler
• CAmazon SageMaker Model Cards
• DAWS AI Service Cards

Explanation

Amazon SageMaker Clarify is the correct choice because it is specifically designed to detect bias in ML models and datasets, and provides explainability reports that clarify how features contribute to model predictions - both critical requirements for a loan approval scenario where fairness and transparency are legally important.

Why the distractors are wrong:

• B. SageMaker Data Wrangler is a data preparation and feature engineering tool - it helps clean and transform data but does not detect model bias or explain predictions.
• C. SageMaker Model Cards is a documentation tool used to record model details, intended use, and evaluation results for governance purposes - not for active bias detection or explainability.
• D. AWS AI Service Cards are informational documents published by AWS that describe how AWS's own pre-built AI services work and their responsible AI practices - they are not a deployable solution for your custom model.

Memory Tip: Think of Clarify = Clarity - it clarifies two things: bias (is the model fair?) and explainability (why did the model make that decision?). Whenever an exam question mentions both bias detection and model explainability together, SageMaker Clarify is almost always the answer.

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