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AI Implementation and OperationseasyMultiple SelectObjective-mapped

AI0-001 AI Implementation and Operations Practice Question

Which THREE are common pitfalls when operationalizing AI models? (Select THREE.)

⚠ Common exam trap

CompTIA often tests the distinction between operational pitfalls and best practices, so the trap here is that candidates may mistake a recommended practice (like using simpler models or automating retraining) for a pitfall, when in fact the pitfall is the lack of monitoring or ignoring scalability.

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

Training-serving skew due to differences in data preprocessing

Training-serving skew occurs when the data preprocessing logic used during model training differs from that used during inference in production. This is a common pitfall in operationalizing AI models, as even minor discrepancies in feature engineering, normalization, or encoding can cause significant performance degradation. For example, using different libraries or versions for tokenization between training and serving pipelines directly leads to skew.

Answer analysis

Option-by-option breakdown

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

  • Training-serving skew due to differences in data preprocessing

    Why this is correct

    Causes model inaccuracies in production.

  • Using simpler models that are easier to debug

    Why it's wrong here

    Simpler models are often preferred for operationalization.

  • Lack of monitoring for model performance drift

    Why this is correct

    Without monitoring, degradation goes unnoticed.

  • Ignoring infrastructure scalability requirements

    Why this is correct

    Leads to resource contention and failures.

  • Automating the model retraining process

    Why it's wrong here

    Automation is a best practice, not a pitfall.

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Written by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

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