hardMultiple Choice
Generative AI Leader Practice Question: A company has deployed a GenAI-powered report…
A company has deployed a GenAI-powered report generation system using Vertex AI. They notice that the cost is higher than expected. Investigation shows that many requests include very long prompts with repetitive boilerplate text. Which cost optimization 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
✓
Enable context caching for repeated prompt prefixes
Caching repeated prompt prefixes can significantly reduce token usage and cost. Batch requests help with throughput but not with per-request token savings. Reducing model size may hurt quality. Ignoring is not a strategy.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the batch size for batch requests
Why it's wrong here
Batching amortises per-request overhead but each request still bills for its full prompt tokens, so repetitive boilerplate cost is unchanged. It is tempting because batch processing lowers unit cost, and would be correct for high-volume, latency-tolerant workloads rather than for trimming redundant prompt content.
- ✓
Enable context caching for repeated prompt prefixes
Why this is correct
Context caching stores the processed repeated prompt prefix so Vertex AI reuses it across requests instead of reprocessing the boilerplate each time. This directly cuts input token charges, targeting the repetitive-prefix pattern identified as the cost driver.
- ✗
Switch to a smaller model size
Why it's wrong here
A smaller model reduces per-token pricing but still processes the same repetitive boilerplate tokens, so the dominant cost driver remains. It is tempting because model choice is a standard cost lever, and would be correct where output quality tolerates reduced capability rather than where input prompts are bloated.
- ✗
Ignore the cost increase as it will stabilize
Why it's wrong here
Costs scale with prompt tokens, so repetitive boilerplate keeps inflating spend rather than stabilising on its own. It is tempting because some workloads plateau once traffic settles, and would be correct only where the cost rise is genuinely transient, not driven by persistent prompt bloat.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This Generative AI Leader practice question is part of Courseiva's free Google Cloud 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 Generative AI Leader exam.