AI0-001 AI Infrastructure and Technologies Practice Question
A team is using a cloud AI service with a pay-per-token pricing model. They want to minimize costs while maintaining response quality. Which strategy is MOST effective?
⚠ Common exam trap
Candidates often mistakenly think that reducing model size or output length is the only way to cut costs, but the correct strategy leverages architectural features like prompt caching to reduce token consumption without affecting quality.
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 prompt caching for repeated query patterns
Prompt caching reduces costs by avoiding redundant token processing for repeated query patterns. The cloud AI service charges per token, so caching the prefix of frequent requests (e.g., system prompts or common context) means only the new, unique tokens are billed, directly lowering expenditure without sacrificing response quality.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Switch to a smaller, less capable model
Why it's wrong here
A smaller model reduces per-token cost but lowers reasoning and accuracy, breaching the requirement to maintain response quality. It is tempting when workloads are simple and cost dominates, but here the stem explicitly demands preserved quality, so prompt optimisation and caching are the effective levers.
- ✗
Increase the batch size for API calls
Why it's wrong here
Batching groups independent requests into one call, which amortises per-call overhead but does not reduce the tokens billed per request; token-based pricing charges for input and output tokens regardless of batching. It is tempting because batching genuinely cuts request counts and latency, and would be right where pricing is per-API-call rather than per-token.
- ✓
Use prompt caching for repeated query patterns
Why this is correct
Prompt caching stores previously processed prompt prefixes so repeated query patterns reuse cached context rather than resending and reprocessing full tokens. Since billing is per token, this directly reduces the tokens charged while preserving identical response quality, satisfying the stem's cost-minimisation constraint.
- ✗
Reduce the model's max_tokens to a very low value
Why it's wrong here
Capping max_tokens truncates responses, cutting output tokens but risking incomplete or degraded answers that fail the quality requirement. It is tempting because output tokens are billed, yet the effective strategy targets prompt size and caching rather than starving the model's response length.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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