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AI Implementation and OperationshardMultiple ChoiceObjective-mapped

AI0-001 AI Implementation and Operations Practice Question

An organization uses a batch prediction pipeline that processes daily customer data to generate marketing recommendations. One month after deployment, the model's performance degrades significantly. The data pipeline logs show that the input data schema has changed — a new categorical feature 'customer_segment' has been added, and the existing feature 'age_group' is now missing. Which step should the operations team take first?

⚠ Common exam trap

CompTIA often tests the misconception that retraining the model (Option A) is the first step to fix performance degradation, but the trap here is that the root cause is a schema mismatch in the preprocessing layer, not the model weights.

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

Update the data preprocessing pipeline to handle missing features and add the new feature

The immediate priority is to ensure the data preprocessing pipeline can handle the schema change without breaking. The pipeline must gracefully handle the missing 'age_group' feature (e.g., by imputing or dropping it) and incorporate the new 'customer_segment' feature before any model retraining or rollback. This prevents data drift from causing inference errors and maintains pipeline stability.

Answer analysis

Option-by-option breakdown

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

  • Retrain the model using the new schema and redeploy

    Why it's wrong here

    Retraining requires a clean dataset; the pipeline must first be fixed to handle the schema.

  • Update the data preprocessing pipeline to handle missing features and add the new feature

    Why this is correct

    This adapts the pipeline to the new schema, enabling proper feeding to the model.

  • Revert to the previous week's model version that was performing well

    Why it's wrong here

    The previous model also expects the old schema; it will fail on new data.

  • Contact the data engineering team to revert the schema change

    Why it's wrong here

    This is not a direct solution and may not be feasible; the operations team can fix preprocessing.

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