- A
Increase the max output token count to 2048 and decrease temperature to 0.1.
Why wrong: Increasing max tokens allows longer output, opposite of the goal.
- B
Refine the prompt to be concise and include instructions to stick to facts and limit output to 50 words.
Clear constraints in the prompt directly control length and hallucination.
- C
Add three few-shot examples of short, factual descriptions.
Why wrong: Few-shot examples guide style but may not be sufficient to enforce brevity and factuality reliably.
- D
Set temperature to 0.0 and top_k to 1.
Why wrong: This reduces creativity but does not prevent the model from inventing details.
Question 466 of 997
Generative AI Leader Practice Question: Techniques to Improve Generative AI Model Output
This Generative AI Leader practice question tests your understanding of techniques to improve generative ai model output. 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 data scientist is using the Gemini API to generate product descriptions for an e-commerce site. The descriptions are often too verbose and include speculative claims that are not in the product specifications. The scientist wants to reduce hallucinations and control the length of the output without retraining the model. What should they do?
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
✓
Refine the prompt to be concise and include instructions to stick to facts and limit output to 50 words.
Option B is correct because refining the prompt to be concise and include explicit instructions to stick to facts and limit output to 50 words directly addresses both issues without retraining. Prompt engineering is the most effective technique for controlling output length and reducing hallucinations in the Gemini API, as it guides the model's behavior through natural language constraints rather than altering generation parameters.
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.
- ✗
Increase the max output token count to 2048 and decrease temperature to 0.1.
Why it's wrong here
Increasing max tokens allows longer output, opposite of the goal.
- ✓
Refine the prompt to be concise and include instructions to stick to facts and limit output to 50 words.
Why this is correct
Clear constraints in the prompt directly control length and hallucination.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Add three few-shot examples of short, factual descriptions.
Why it's wrong here
Few-shot examples guide style but may not be sufficient to enforce brevity and factuality reliably.
- ✗
Set temperature to 0.0 and top_k to 1.
Why it's wrong here
This reduces creativity but does not prevent the model from inventing details.
Common exam traps
Common exam trap: answer the scenario, not the keyword
This exam often tests the misconception that adjusting generation parameters like temperature or top_k is the primary way to control factual accuracy and length, when in fact prompt engineering is the most direct and effective method for these specific requirements without retraining.
Trap categories for this question
Command / output trap
Increasing max tokens allows longer output, opposite of the goal.
Detailed technical explanation
How to think about this question
Prompt engineering leverages the model's instruction-following capability, which is fine-tuned to prioritize explicit directives in the system or user prompt. The Gemini API processes these instructions as part of its context, effectively constraining the output distribution without modifying model weights. In practice, combining a concise prompt with a word limit (e.g., '50 words') and a factual constraint (e.g., 'only use information from the product specs') often yields better results than parameter tuning alone, because it directly shapes the model's reasoning path.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
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FAQ
Questions learners often ask
What does this Generative AI Leader question test?
Techniques to Improve Generative AI Model Output — This question tests Techniques to Improve Generative AI Model Output — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Refine the prompt to be concise and include instructions to stick to facts and limit output to 50 words. — Option B is correct because refining the prompt to be concise and include explicit instructions to stick to facts and limit output to 50 words directly addresses both issues without retraining. Prompt engineering is the most effective technique for controlling output length and reducing hallucinations in the Gemini API, as it guides the model's behavior through natural language constraints rather than altering generation parameters.
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.
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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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