- A
Fine-tune the model on summaries that are exactly 200 words.
Why wrong: Overkill and time-consuming when a simple parameter setting works.
- B
Set the max output tokens parameter to 200.
Max token limits directly truncate the output, enforcing the length constraint.
- C
Add a system prompt that says 'Summarize in exactly 200 words.'
Why wrong: Prompt instructions may be ignored or inaccurately followed; a hard token limit is more reliable.
- D
Lower the temperature to reduce variability in word choices.
Why wrong: Temperature does not control output length.
Question 48 of 997
Set Max Output Tokens for Consistent Summary Length
This Generative AI Leader practice question tests your understanding of techniques to improve generative ai model output. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 team uses a generative model to summarize lengthy legal documents. The summaries are accurate but often exceed the target length of 200 words, varying widely. Which simple adjustment should be applied to ensure consistent output length?
Quick Answer
The correct answer is to set the max output tokens parameter to 200. This adjustment directly enforces a hard cap on the model’s generation, ensuring that every summary stops at the specified token limit, which reliably controls output length regardless of the input document’s complexity. On the Google Cloud Generative AI Leader exam, this question tests your understanding of inference parameters versus more resource-intensive methods like fine-tuning; a common trap is assuming prompt engineering alone can guarantee strict length adherence, but models often ignore such instructions. The search intent behind “controlling output length with max tokens” highlights that this parameter is the simplest, most deterministic lever for consistent results. Remember: tokens are the model’s ruler—set the max, and the output will not exceed it.
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
✓
Set the max output tokens parameter to 200.
Setting the max output tokens parameter to 200 directly caps the number of tokens the model can generate, ensuring the summary cannot exceed the target length. This is a deterministic, model-level constraint that works regardless of prompt phrasing or training data, making it the most reliable adjustment for consistent output 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.
- ✗
Fine-tune the model on summaries that are exactly 200 words.
Why it's wrong here
Overkill and time-consuming when a simple parameter setting works.
- ✓
Set the max output tokens parameter to 200.
Why this is correct
Max token limits directly truncate the output, enforcing the length constraint.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Add a system prompt that says 'Summarize in exactly 200 words.'
Why it's wrong here
Prompt instructions may be ignored or inaccurately followed; a hard token limit is more reliable.
- ✗
Lower the temperature to reduce variability in word choices.
Why it's wrong here
Temperature does not control output length.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google often tests the misconception that prompt engineering or fine-tuning can enforce precise numerical constraints, when in fact only a hard token limit parameter provides deterministic control over output length.
Trap categories for this question
Command / output trap
Temperature does not control output length.
Detailed technical explanation
How to think about this question
The max output tokens parameter is enforced at the decoding stage by truncating the generated sequence once the token count reaches the specified limit, regardless of whether the model has finished a sentence. In practice, this can cut off mid-sentence, so a common real-world approach is to set the limit slightly higher (e.g., 250 tokens) and then post-process to trim to the exact word count, or use a stop sequence. Under the hood, the model's attention mask and logit processing stop generating new tokens once the limit is hit, making it a hard constraint unlike soft prompt instructions.
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: Set the max output tokens parameter to 200. — Setting the max output tokens parameter to 200 directly caps the number of tokens the model can generate, ensuring the summary cannot exceed the target length. This is a deterministic, model-level constraint that works regardless of prompt phrasing or training data, making it the most reliable adjustment for consistent output 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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