- A
Batch similar requests to reduce per-request overhead
Batching reduces the number of API calls and can lower cost.
- B
Use a larger model with higher accuracy to further increase CTR
Why wrong: A larger model would increase cost further, opposite of the goal.
- C
Increase the number of few-shot examples in the prompt
Why wrong: More examples increase token usage and cost without necessarily improving CTR.
- D
Switch to a smaller model and re-A/B test to confirm CTR impact
A smaller model may still achieve the same CTR at lower cost; testing is needed.
- E
Implement response caching for repeated product SKUs
Caching avoids regenerating descriptions for the same SKU, reducing cost.
Question 867 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 deploying a GenAI system that generates product descriptions. During A/B testing, the new system shows a 20% increase in click-through rate (CTR) but a 15% increase in average cost per query due to the model size. The team wants to optimize cost without sacrificing the CTR gain. Which THREE actions should they take? (Choose three.)
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
✓
Batch similar requests to reduce per-request overhead
Option A is correct because batching similar requests reduces the per-request overhead by combining multiple inference calls into a single batch, which amortizes the fixed costs (e.g., model loading, token processing) across more outputs. This directly lowers the average cost per query while preserving the model architecture and CTR gains, as the model's output quality remains unchanged.
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.
- ✓
Batch similar requests to reduce per-request overhead
Why this is correct
Batching reduces the number of API calls and can lower cost.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use a larger model with higher accuracy to further increase CTR
Why it's wrong here
A larger model would increase cost further, opposite of the goal.
- ✗
Increase the number of few-shot examples in the prompt
Why it's wrong here
More examples increase token usage and cost without necessarily improving CTR.
- ✓
Switch to a smaller model and re-A/B test to confirm CTR impact
Why this is correct
A smaller model may still achieve the same CTR at lower cost; testing is needed.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Implement response caching for repeated product SKUs
Why this is correct
Caching avoids regenerating descriptions for the same SKU, reducing cost.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google often tests the misconception that adding more few-shot examples always improves output quality, but in reality, it increases token costs and can degrade performance due to context window limits or irrelevant examples.
Detailed technical explanation
How to think about this question
Batching works by concatenating multiple input sequences into a single tensor, leveraging GPU parallelism to process them simultaneously, which reduces latency per query and improves throughput. In transformer-based models, the attention mechanism scales quadratically with sequence length, so batching short sequences is more efficient than processing them individually. Real-world deployments often use dynamic batching with a maximum batch size to balance memory usage and cost savings.
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: Batch similar requests to reduce per-request overhead — Option A is correct because batching similar requests reduces the per-request overhead by combining multiple inference calls into a single batch, which amortizes the fixed costs (e.g., model loading, token processing) across more outputs. This directly lowers the average cost per query while preserving the model architecture and CTR gains, as the model's output quality remains unchanged.
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
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Last reviewed: Jul 4, 2026
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