- A
Fine-tune a smaller model on the specific task
Why wrong: Fine-tuning incurs additional training costs; using a smaller pre-trained model is simpler.
- B
Implement batch processing to group requests
Why wrong: Batch processing reduces per-request overhead but does not address the high per-token cost of a large model.
- C
Reduce the model's temperature to 0.0
Why wrong: Temperature adjustment does not affect token pricing; cost is based on model size and token count.
- D
Switch to a smaller model like Gemini 1.5 Flash and use structured prompts
A smaller model is cheaper per token and sufficient for simple descriptions; structured prompts maintain quality.
Question 41 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 migrating a GenAI proof-of-concept to production. During the pilot, they used a large model (e.g., Gemini 1.5 Pro) and incurred high costs. The use case is simple: generating short product descriptions from structured data. Which cost optimization strategy should they implement first?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"first"Why it matters: Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
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
✓
Switch to a smaller model like Gemini 1.5 Flash and use structured prompts
Option D is correct because the primary cost driver in this scenario is the model size itself. Since the use case is simple (generating short product descriptions from structured data), a smaller model like Gemini 1.5 Flash can handle the task with significantly lower inference cost per token. Structured prompts further optimize by reducing token waste and ensuring consistent output, making this the most direct and impactful first step.
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.
- ✗
Fine-tune a smaller model on the specific task
Why it's wrong here
Fine-tuning incurs additional training costs; using a smaller pre-trained model is simpler.
- ✗
Implement batch processing to group requests
Why it's wrong here
Batch processing reduces per-request overhead but does not address the high per-token cost of a large model.
- ✗
Reduce the model's temperature to 0.0
Why it's wrong here
Temperature adjustment does not affect token pricing; cost is based on model size and token count.
- ✓
Switch to a smaller model like Gemini 1.5 Flash and use structured prompts
Why this is correct
A smaller model is cheaper per token and sufficient for simple descriptions; structured prompts maintain quality.
Clue confirmation
The clue word "first" in the question point toward this answer.
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 fine-tuning is the first step for any production optimization, when in reality, model selection and prompt engineering are cheaper and faster to implement for simple tasks.
Detailed technical explanation
How to think about this question
Under the hood, model inference cost scales roughly with the number of parameters and the number of tokens processed. Gemini 1.5 Pro uses a Mixture-of-Experts (MoE) architecture with a large number of active parameters per token, whereas Gemini 1.5 Flash is a smaller, more efficient variant designed for latency-sensitive and cost-sensitive tasks. Structured prompts (e.g., using JSON schemas or few-shot examples) reduce the number of input tokens needed and guide the model to produce concise outputs, further lowering cost per request.
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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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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: Switch to a smaller model like Gemini 1.5 Flash and use structured prompts — Option D is correct because the primary cost driver in this scenario is the model size itself. Since the use case is simple (generating short product descriptions from structured data), a smaller model like Gemini 1.5 Flash can handle the task with significantly lower inference cost per token. Structured prompts further optimize by reducing token waste and ensuring consistent output, making this the most direct and impactful first step.
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.
Are there clue words in this question I should notice?
Yes — watch for: "first". Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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