AIF-C01 Guidelines for Responsible AI Practice Question
A financial services company must comply with regulatory requirements that mandate explainability of credit scoring models. They have deployed a model using SageMaker and need to generate reports showing feature importance for each prediction. Which combination of services should they use to automate this?
⚠ Common exam trap
AWS often tests the distinction between monitoring (Model Monitor) and explainability (Clarify), so the trap here is that candidates confuse 'monitoring model performance' with 'explaining individual predictions' and pick Option A.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
SageMaker Clarify + SageMaker Pipelines
SageMaker Clarify provides built-in explainability capabilities, including feature importance for individual predictions via SHAP values, which directly addresses the regulatory requirement for model explainability. SageMaker Pipelines automates the end-to-end workflow, allowing you to schedule and run Clarify processing jobs to generate reports on a recurring basis without manual intervention.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
SageMaker Model Monitor + Amazon QuickSight
Why it's wrong here
Model Monitor detects data and model drift, not per-prediction feature attributions, so it cannot produce the required explainability reports. It is tempting because it is the SageMaker observability service, and would be correct for monitoring endpoint data quality or bias drift over time rather than regulatory feature-importance reporting.
- ✗
SageMaker Ground Truth + AWS Lambda
Why it's wrong here
Ground Truth labels training data and Lambda runs event-driven code; neither computes per-prediction feature attributions. They suit dataset annotation and orchestration respectively, but explainability reports require SageMaker Clarify, which generates feature importance and bias metrics.
- ✓
SageMaker Clarify + SageMaker Pipelines
Why this is correct
SageMaker Clarify computes feature attributions, such as SHAP values, for individual predictions, while SageMaker Pipelines orchestrates and automates the report generation workflow. Together they satisfy the regulatory explainability mandate by producing per-prediction feature-importance reports without manual intervention.
- ✗
SageMaker Data Wrangler + SageMaker Studio
Why it's wrong here
Data Wrangler profiles and transforms datasets during preparation, and Studio is a development IDE; neither computes per-prediction feature attributions. It is tempting because both are SageMaker services used for data analysis, and would be correct for exploratory feature engineering before training rather than automated explainability reporting.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AIF-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AIF-C01 exam.