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AIF-C01 Practice Question: Reduce the cost of running a large number of…

A company wants to reduce the cost of running a large number of inference requests for a text classification task. The responses can tolerate a slight delay. Which cost optimization strategy should they implement?

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

Use batch inference to process multiple requests asynchronously

Batch inference processes multiple requests together, reducing cost per request at the expense of higher latency. Model caching can help if there are many repeated requests, but batch inference is more general. Switching to a larger model increases cost. Using a vector store is unrelated.

Answer analysis

Option-by-option breakdown

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

  • Store the training data in a vector store for faster retrieval

    Why it's wrong here

    Vector stores are for RAG, not for reducing inference costs on classification tasks.

  • Use batch inference to process multiple requests asynchronously

    Why this is correct

    Batch inference groups requests, lowering per-request cost, and is suitable when real-time responses are not required.

  • Use a larger, more accurate model to reduce the number of retries

    Why it's wrong here

    Larger models are more expensive per request and may increase costs, not reduce them.

  • Implement model caching to reuse results for identical prompts

    Why it's wrong here

    Caching helps if prompts repeat, but batch inference addresses volume more directly.

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JA

Written by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

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.