AIF-C01 Guidelines for Responsible AI Practice Question

After deploying a model, a company notices that the distribution of the input features has shifted compared to the training data. Which feature of Amazon SageMaker Model Monitor can alert them to this change?

⚠ Common exam trap

Many candidates confuse 'data quality monitoring' (input feature drift) with 'model quality monitoring' (prediction performance metrics), as both involve 'quality' but address entirely different aspects of the ML pipeline.

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

✓

Data quality monitoring

Amazon SageMaker Model Monitor's data quality monitoring feature is specifically designed to detect changes in the distribution of input features compared to the training data. It uses statistical tests (e.g., Kolmogorov-Smirnov, Chi-squared) to compare baseline and live data distributions, alerting when drift is detected. This directly addresses the scenario of input feature distribution shift.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
Model quality monitoring
Why it's wrong here
Model quality monitors prediction accuracy against ground truth, not feature drift.
✗
Bias drift monitoring
Why it's wrong here
Bias drift monitors fairness metrics, not input feature distributions.
✗
Feature importance drift
Why it's wrong here
SageMaker Model Monitor does not have a built-in feature importance drift monitor.
✓
Data quality monitoring
Why this is correct
Data quality monitors for drift in input features (baseline vs. live).

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AIF-C01 Guidelines for Responsible AI Practice Question

Why each option matters

Option-by-option breakdown

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