- A
Use batch API requests for multiple sensor readings
Batch requests reduce per-token cost.
- B
Use the largest available model to ensure accuracy
Why wrong: Larger models cost more; choose the smallest model that meets accuracy needs.
- C
Use structured output formatting in the prompt (e.g., 'Return JSON')
Explicitly requesting JSON improves reliability.
- D
Choose the smallest model that meets accuracy requirements
Smaller models have lower cost per token.
- E
Include multiple few-shot examples of JSON in every prompt
Why wrong: Few-shot examples increase token count and cost.
Question 962 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 manufacturing company wants to use GenAI to generate maintenance reports from sensor data. They need structured output (JSON) for downstream systems, and they want to reduce token costs. Which THREE strategies should they use?
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
✓
Use batch API requests for multiple sensor readings
Structured output ensures JSON format; batch requests reduce cost; the smallest suitable model minimizes token usage. Few-shot adds tokens; caching may not help for diverse sensor data.
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.
- ✓
Use batch API requests for multiple sensor readings
Why this is correct
Batch requests reduce per-token cost.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use the largest available model to ensure accuracy
Why it's wrong here
Larger models cost more; choose the smallest model that meets accuracy needs.
- ✓
Use structured output formatting in the prompt (e.g., 'Return JSON')
Why this is correct
Explicitly requesting JSON improves reliability.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Choose the smallest model that meets accuracy requirements
Why this is correct
Smaller models have lower cost per token.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Include multiple few-shot examples of JSON in every prompt
Why it's wrong here
Few-shot examples increase token count and cost.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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.
Identify which Generative AI Leader exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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Applying Generative AI in Business — study guide chapter
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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: Use batch API requests for multiple sensor readings — Structured output ensures JSON format; batch requests reduce cost; the smallest suitable model minimizes token usage. Few-shot adds tokens; caching may not help for diverse sensor data.
What should I do if I get this Generative AI Leader question wrong?
Identify which Generative AI Leader exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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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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