hardMultiple ChoiceObjective-mapped
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 reduces per-request overhead but does not reduce the number of tokens processed per request.
- ✓
Enable context caching for repeated prompt prefixes
Why this is correct
Context caching stores repeated prefixes so they are not re-sent with each request, reducing token consumption and cost.
- ✗
Switch to a smaller model size
Why it's wrong here
Smaller models may reduce per-token cost but can produce lower quality; the prompt length issue remains.
- ✗
Ignore the cost increase as it will stabilize
Why it's wrong here
Ignoring cost is not a strategy; the issue should be addressed proactively.
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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.