AIF-C01 Practice Question: Security, Compliance, and Governance for AI Solutions
A company has deployed a model on Amazon SageMaker and enabled Model Monitor. They notice that the model's prediction accuracy has declined over time. Which type of drift is this, and what should they do?
⚠ Common exam trap
The trap is confusing concept drift with data drift; candidates may pick data drift because both cause accuracy loss, but concept drift specifically refers to the changed relationship between features and target.
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
✓
This is concept drift; they should retrain the model with new data
The decline in prediction accuracy over time, despite the model and data pipeline remaining the same, is characteristic of concept drift—the relationship between input features and the target variable has changed. The appropriate action is to retrain the model with new data that reflects the current relationship. SageMaker Model Monitor can detect this via accuracy metrics.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
This is bias drift; they should run SageMaker Clarify
Why it's wrong here
Accuracy decline from a deployed model is concept drift, not bias drift, so SageMaker Clarify — which detects bias in data and predictions — does not address it. Clarify is tempting because it also monitors models, but it is the right choice when the requirement is measuring fairness across groups rather than tracking prediction accuracy degradation.
- ✓
This is concept drift; they should retrain the model with new data
Why this is correct
Concept drift occurs when the statistical relationship between input features and the target variable changes, degrading accuracy even if input distributions remain stable. Retraining with recent data captures the new feature-to-label mapping, directly addressing the declining accuracy described in the stem.
- ✗
This is model drift; they should delete and redeploy the model
Why it's wrong here
Deleting and redeploying discards the trained artefacts and does not diagnose the cause; drift requires investigation and retraining with current data. Model Monitor detects data and concept drift, so the response is to analyse metrics, then retrain or update the baseline rather than redeploy blindly.
- ✗
This is data drift; they should update the training data
Why it's wrong here
Accuracy decline from changing input-output relationships is concept drift, not data drift; retraining on the same distribution won't fix it. Data drift means the input feature distribution shifts, where updating training data with recent samples would be the right response.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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.