AI0-001 AI Infrastructure and Technologies Practice Question
A company uses Azure OpenAI to generate customer support responses. The team notices that repeated queries with similar context incur high costs due to token usage. They want to reduce costs without affecting response quality. Which strategy is MOST effective?
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
✓
Implement prompt caching
Prompt caching stores and reuses tokens from previous queries, reducing token consumption for similar requests and lowering costs without quality loss.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use a larger model to improve efficiency
Why it's wrong here
A larger model consumes more tokens per call at a higher unit price, raising cost rather than lowering it. It is tempting because larger models often need fewer retries or shorter prompts, and it would be correct when accuracy, not token cost, is the binding constraint.
- ✗
Increase the frequency penalty
Why it's wrong here
Frequency penalty alters token-selection probabilities to discourage repetition; it does not reduce prompt or completion token counts, so billing is unchanged. It is tempting because it targets repeated phrasing, and it would be correct when outputs loop or repeat verbatim and diversity is the goal.
- ✗
Reduce the max_tokens parameter
Why it's wrong here
Capping max_tokens truncates generated output, so longer support answers get cut off, degrading quality. It is tempting because it directly limits completion tokens, and it would be correct when responses must fit a fixed length or cost ceiling where truncation is acceptable.
- ✓
Implement prompt caching
Why this is correct
Prompt caching stores and reuses the computed key-value representations of repeated context, so identical or similar prefixes are billed at a reduced cached-token rate rather than full input tokens. This directly cuts token costs on recurring queries while returning the same model output, preserving response quality.
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