- A
Use the largest available model for maximum accuracy
Why wrong: Larger models increase cost and latency, not aligned with cost control.
- B
Cache frequent queries and their responses
Caching reduces repeated API calls, saving tokens and cost.
- C
Implement Retrieval-Augmented Generation (RAG) to retrieve relevant document chunks
RAG allows the model to answer from the latest documents without retraining.
- D
Use a longer context window to include entire documents in the prompt
Why wrong: Longer contexts increase token count and cost per query, which is counterproductive.
- E
Fine-tune a large language model on the product documentation
Why wrong: Fine-tuning is costly and does not adapt to document updates without retraining.
Question 875 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 wants to build a GenAI-powered customer support chatbot. They require the chatbot to provide accurate answers based on the latest product documentation, and they need to control costs by minimizing token usage. Which TWO 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
✓
Cache frequent queries and their responses
RAG ensures answers are grounded in latest docs. Caching common queries reduces token usage. Fine-tuning is expensive and not dynamic. Using a large model increases cost. Long context windows increase token usage.
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 the largest available model for maximum accuracy
Why it's wrong here
Larger models increase cost and latency, not aligned with cost control.
- ✓
Cache frequent queries and their responses
Why this is correct
Caching reduces repeated API calls, saving tokens and cost.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Implement Retrieval-Augmented Generation (RAG) to retrieve relevant document chunks
Why this is correct
RAG allows the model to answer from the latest documents without retraining.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use a longer context window to include entire documents in the prompt
Why it's wrong here
Longer contexts increase token count and cost per query, which is counterproductive.
- ✗
Fine-tune a large language model on the product documentation
Why it's wrong here
Fine-tuning is costly and does not adapt to document updates without retraining.
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: Cache frequent queries and their responses — RAG ensures answers are grounded in latest docs. Caching common queries reduces token usage. Fine-tuning is expensive and not dynamic. Using a large model increases cost. Long context windows increase token usage.
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
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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