- A
Use few-shot examples and reduce max output tokens
Correct: Few-shot demonstrates concise style; max tokens enforces length limit.
- B
Set top_p to 0.5 and increase repetition penalty
Why wrong: Top_p and repetition penalty do not directly control length or structure.
- C
Use prompt caching and increase batch size
Why wrong: Caching and batch size affect performance, not output quality.
- D
Increase temperature and use a larger model
Why wrong: Higher temperature increases randomness; larger model does not guarantee conciseness.
Question 371 of 997
Few-Shot Examples and Max Tokens for Concise Summaries
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.
For a document summarization task, a team wants to produce concise summaries without losing key information. Which combination of techniques is most effective?
Quick Answer
The correct combination is to use few-shot examples and reduce the max output tokens. Few-shot examples teach the model the desired summary format and level of detail by providing input-output pairs, while reducing the max output tokens enforces a strict length limit, preventing verbosity and ensuring conciseness without sacrificing critical information. On the Google Cloud Generative AI Leader exam, this question tests your understanding of how to control model output for specific tasks like summarization, where the trap is choosing options that increase token limits or rely on prompt engineering alone without explicit constraints. A common memory tip is to think of few-shot examples as the “template” and max tokens as the “cage”—together they shape and confine the response to be both relevant and brief.
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 few-shot examples and reduce max output tokens
Option A is correct because using few-shot examples provides the model with explicit patterns of desired summarization behavior, guiding it to produce concise outputs, while reducing max output tokens enforces a hard length constraint that prevents verbosity. This combination directly addresses the goal of conciseness without losing key information by conditioning the model on high-quality examples and capping the response length.
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 few-shot examples and reduce max output tokens
Why this is correct
Correct: Few-shot demonstrates concise style; max tokens enforces length limit.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Set top_p to 0.5 and increase repetition penalty
Why it's wrong here
Top_p and repetition penalty do not directly control length or structure.
- ✗
Use prompt caching and increase batch size
Why it's wrong here
Caching and batch size affect performance, not output quality.
- ✗
Increase temperature and use a larger model
Why it's wrong here
Higher temperature increases randomness; larger model does not guarantee conciseness.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google's Gen AI Leader certification often tests the misconception that randomness parameters (temperature, top_p) or model size alone can control output length, when in fact explicit constraints like max tokens and guided examples are required for precise summarization tasks.
Trap categories for this question
Command / output trap
Caching and batch size affect performance, not output quality.
Detailed technical explanation
How to think about this question
Few-shot examples work by conditioning the model's attention on input-output pairs within the same context window, effectively performing in-context learning without fine-tuning; reducing max output tokens truncates the generation at a fixed step count, which interacts with the model's token probability distribution to force early stopping. In practice, this combination is often used with a system prompt like 'Summarize the following text in 3 sentences' alongside a few exemplars, and the max tokens limit must be set carefully to avoid cutting off critical content mid-sentence.
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: Use few-shot examples and reduce max output tokens — Option A is correct because using few-shot examples provides the model with explicit patterns of desired summarization behavior, guiding it to produce concise outputs, while reducing max output tokens enforces a hard length constraint that prevents verbosity. This combination directly addresses the goal of conciseness without losing key information by conditioning the model on high-quality examples and capping the response length.
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
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