- A
Provision reserved throughput for all requests
Why wrong: Reserved throughput is for predictable high volume; may overprovision initially.
- B
Implement response caching for common queries
Caching avoids redundant API calls, reducing token usage and cost.
- C
Select the smallest model that meets quality requirements
Smaller models have lower cost per token, improving scalability.
- D
Use batch API for non-real-time requests
Batch processing offers lower cost for asynchronous workloads.
- E
Conduct A/B testing on model versions
Why wrong: A/B testing is for quality evaluation, not cost predictability.
Question 191 of 997
Generative AI Leader Applying Generative AI in Business Practice Question
This Generative AI Leader practice question tests your understanding of applying generative ai in business. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A company is transitioning a generative AI pilot to production. They need to ensure cost predictability and scalability. Which THREE actions should they take?
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Select the smallest model that meets quality requirements
Why this is correct
Smaller models have lower cost per token, improving scalability.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Use batch API for non-real-time requests
Why this is correct
Batch processing offers lower cost for asynchronous workloads.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Conduct A/B testing on model versions
Why it's wrong here
A/B testing is for quality evaluation, not cost predictability.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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.
Detailed technical explanation
How to think about this question
Response caching typically uses an in-memory store like Redis or a CDN edge cache with TTL-based invalidation. For generative AI, caching is most effective for deterministic outputs (e.g., FAQ answers, template completions) where the same prompt yields the same response. In production, cache hit ratios above 60% can reduce API costs by half while maintaining sub-10ms response times.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this Generative AI Leader question test?
Applying Generative AI in Business — This question tests Applying Generative AI in Business — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this Generative AI Leader question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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Last reviewed: Jul 4, 2026
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.
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