hardMultiple Select
Generative AI Leader Practice Question: Transitioning a generative AI pilot to production
A company is transitioning a generative AI pilot to production. They need to ensure cost predictability and scalability. Which THREE actions should they take?
⚠ Common exam trap
Google often tests the misconception that reserved throughput (Option A) is always the best way to ensure cost predictability, when in fact it can increase costs for spiky workloads and ignores the scalability benefits of caching and batch processing.
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
✓
Implement response caching for common queries
Response caching for common queries reduces latency and API costs by serving repeated requests from a cache instead of invoking the generative AI model each time. This directly improves cost predictability (fewer model invocations) and scalability (reduced load on the model endpoint), making it a core optimization for production deployments.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Provision reserved throughput for all requests
Why it's wrong here
Reserved throughput is for predictable high volume; may overprovision initially.
- ✓
Implement response caching for common queries
Why this is correct
Caching avoids redundant API calls, reducing token usage and cost.
- ✓
Select the smallest model that meets quality requirements
Why this is correct
Smaller models have lower cost per token, improving scalability.
- ✓
Use batch API for non-real-time requests
Why this is correct
Batch processing offers lower cost for asynchronous workloads.
- ✗
Conduct A/B testing on model versions
Why it's wrong here
A/B testing is for quality evaluation, not cost predictability.
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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.