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AIF-C01 Practice Question: A developer needs to reduce costs for a Bedrock…
A developer needs to reduce costs for a Bedrock application that processes high volumes of similar queries. The queries are repetitive and the model is invoked many times with the same prompt. Which cost optimization technique is MOST suitable?
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
✓
Enable model caching to serve repeated prompts from cache
Model caching (prompt caching) stores responses to common prompts, reducing the need to recompute. Batch inference is for asynchronous processing, not real-time. Right-sizing the model helps but does not leverage query repetition. Fine-tuning is for adapting the model, not cost savings on repetitive queries.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Select a smaller right-sized model
Why it's wrong here
A smaller model reduces cost per invocation but does not exploit the repetition of identical prompts.
- ✗
Fine-tune the model on the common queries
Why it's wrong here
Fine-tuning is for improving accuracy on specific tasks, not for cost savings on repetitive invocations.
- ✗
Use batch inference to process multiple queries together
Why it's wrong here
Batch inference improves throughput but does not eliminate repeated computation for identical prompts.
- ✓
Enable model caching to serve repeated prompts from cache
Why this is correct
Caching avoids recomputation for identical prompts, reducing latency and cost.
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