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AI Implementation and OperationseasyMultiple SelectObjective-mapped

AI0-001 AI Implementation and Operations Practice Question

Which TWO actions are most appropriate for managing model drift in a production AI system?

⚠ Common exam trap

CompTIA often tests the distinction between reactive fixes (like rollback) and proactive, automated strategies (like monitoring and retraining), tricking candidates into choosing rollback as a valid long-term drift management action.

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

Periodically retrain the model on recent data

Periodically retraining the model on recent data is a fundamental strategy to combat model drift, ensuring the model adapts to changes in the underlying data distribution (e.g., concept drift or covariate shift). This aligns with MLOps best practices for maintaining model accuracy over time in production AI systems.

Answer analysis

Option-by-option breakdown

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

  • Freeze the model to prevent any changes

    Why it's wrong here

    Freezing the model would ignore drift and degrade performance.

  • Roll back to a previous model version if performance degrades

    Why it's wrong here

    Rollback is a reactive measure, not a proactive management strategy.

  • Periodically retrain the model on recent data

    Why this is correct

    Regular retraining helps the model adapt to new patterns.

  • Manually review all model predictions

    Why it's wrong here

    Manual review is not scalable or efficient for drift management.

  • Implement automated monitoring to detect drift indicators

    Why this is correct

    Automated monitoring enables early detection of drift.

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Last reviewed: Jun 30, 2026

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