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
Smaller models may reduce cost but can degrade response quality for complex tasks.
- ✗
Increase the batch size for API calls
Why it's wrong here
Batching may reduce per-token cost but does not affect token consumption per request; caching directly reduces token usage.
- ✓
Use prompt caching for repeated query patterns
Why this is correct
Caching avoids reprocessing identical prompts, saving token costs and reducing latency while preserving quality.
- ✗
Reduce the model's max_tokens to a very low value
Why it's wrong here
Truncating output may hurt quality; some responses need longer completions.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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