hardMultiple Choice
AIF-C01 Practice Question: A company uses Amazon SageMaker to host a model…
A company uses Amazon SageMaker to host a model for fraud detection. The model must be re-evaluated for bias on a monthly basis. Which SageMaker feature can be used to detect bias in a trained model?
⚠ Common exam trap
Candidates often confuse SageMaker Model Monitor (which monitors data drift) with bias detection, but Model Monitor does not evaluate model fairness or bias metrics.
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 Clarify is the correct choice because it is specifically designed to detect bias in machine learning models and data. It provides built-in capabilities to evaluate bias metrics (e.g., difference in positive proportions, disparate impact) both before training (pre-training bias) and after training (post-training bias), making it suitable for the monthly re-evaluation requirement.
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 Debugger
Why it's wrong here
Debugger captures tensors and metrics during training to diagnose convergence and vanishing gradients, not post-training bias. It is tempting because it inspects model internals, but fairness evaluation of a trained model requires Clarify, which computes bias metrics on data and predictions.
- ✗
SageMaker Model Monitor
Why it's wrong here
Model Monitor detects data drift and quality deviations in deployed endpoints by comparing live traffic against a baseline, not statistical bias across protected groups. It is tempting because it runs monthly on hosted models, but Clarify provides the bias and fairness metrics this requirement demands.
- ✓
SageMaker Clarify
Why this is correct
SageMaker Clarify runs bias detection on trained models, computing metrics such as disparate impact across facets before and after training. It satisfies the monthly re-evaluation requirement by analysing the model directly, unlike monitoring or debugging tools.
- ✗
SageMaker Autopilot
Why it's wrong here
Autopilot automates model selection, training and tuning for tabular datasets; it does not measure bias in an already-trained model. SageMaker Clarify provides bias detection and explainability. Autopilot suits teams wanting automated model building without manual algorithm choice.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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