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AI Implementation and OperationsmediumMultiple ChoiceObjective-mapped

AI0-001 AI Implementation and Operations Practice Question

A company is deploying a fraud detection model that must return predictions within 100ms to avoid transaction delays. The team is deciding between batch and real-time inference. Which factor most strongly supports a real-time inference architecture?

⚠ Common exam trap

CompTIA often tests the misconception that batch inference is always cheaper or more efficient, but the trap here is that latency requirements (under 100ms) force a real-time architecture regardless of cost or data volume.

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

The application requires immediate feedback for each transaction

Real-time inference is required when the application must return predictions within strict latency bounds (e.g., 100ms) to avoid transaction delays. The need for immediate feedback per transaction directly aligns with a real-time architecture, where each request is processed individually as it arrives, rather than waiting for a batch window. Batch inference would introduce unacceptable latency because it processes groups of records on a schedule, not on-demand.

Answer analysis

Option-by-option breakdown

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

  • The model requires large amounts of historical data for each prediction

    Why it's wrong here

    Real-time inference typically uses recent data, not large historical context.

  • The application requires immediate feedback for each transaction

    Why this is correct

    Real-time inference delivers low-latency predictions for each request.

  • The infrastructure budget is limited and must be optimized

    Why it's wrong here

    Batch is often cheaper, but latency trumps cost here.

  • The model can be retrained weekly using gathered data

    Why it's wrong here

    Retraining frequency is separate from inference mode.

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