Quick Answer
The answer is `max_tokens`. This parameter directly controls the maximum number of tokens—which can be words, subwords, or characters—that the model is allowed to generate in a single response. By decreasing the `max_tokens` value, you explicitly cap the output length, making it the most straightforward way to reduce verbosity in Amazon Bedrock’s generated text. On the AWS Certified AI Practitioner AIF-C01 exam, this concept tests your understanding of foundational inference parameters, often appearing in scenario-based questions where a candidate must choose the correct knob to turn for concise output. A common trap is confusing `max_tokens` with `temperature` or `top_p`, which control randomness and diversity, not length. Remember: if the output is too long, you cut the token budget—think of it as setting a word count limit in a document. A helpful memory tip is to associate “max” with “maximum length” and “tokens” with the building blocks of the response, so decreasing `max_tokens` directly shrinks the generated text.
This AIF-C01 practice question tests your understanding of applications of foundation models. 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 uses Amazon Bedrock to generate marketing copy. The summaries are too verbose. Which parameter should be decreased to directly limit the length of the output?
max_tokens
max_tokens sets the maximum number of tokens in the response.
temperature
Why wrong: Temperature controls randomness, not output length.
top_p
Why wrong: top_p affects nucleus sampling, not length.
frequency_penalty
Why wrong: Frequency penalty discourages repetition, but does not directly limit length.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
max_tokens
The `max_tokens` parameter directly controls the maximum number of tokens (words or subwords) in the generated output. By decreasing this value, you explicitly cap the length of the marketing copy, making it less verbose. This is the most direct way to limit output length in Amazon Bedrock and other LLM APIs.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
max_tokens
Why this is correct
max_tokens sets the maximum number of tokens in the response.
Related concept
Read the scenario before looking for a memorised answer.
temperature
Why it's wrong here
Temperature controls randomness, not output length.
top_p
Why it's wrong here
top_p affects nucleus sampling, not length.
frequency_penalty
Why it's wrong here
Frequency penalty discourages repetition, but does not directly limit length.
Common exam traps
The trap here is that candidates confuse parameters that affect output style (temperature, top_p, frequency_penalty) with the one that directly controls output length (max_tokens), leading them to pick a parameter that changes how the model writes rather than how much it writes.
Trap categories for this question
Command / output trap
Temperature controls randomness, not output length.
Detailed technical explanation
Under the hood, `max_tokens` defines the stopping condition for autoregressive generation: the model stops producing tokens once the count reaches this limit, even if the response is incomplete. A real-world scenario is generating concise product descriptions where exceeding a certain token count would break a UI character limit; decreasing `max_tokens` ensures compliance without altering the model's style or creativity.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Applications of Foundation Models — study guide chapter
Learn the concepts, then practise the questions
Applications of Foundation Models practice questions
Targeted practice on this topic area only
All AIF-C01 questions
500 questions across all exam domains
AWS Certified AI Practitioner AIF-C01 study guide
Full concept coverage aligned to exam objectives
AIF-C01 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Practise AIF-C01 questions linked to Applications of Foundation Models.
Practise AIF-C01 questions linked to Fundamentals of AI and ML.
Practise AIF-C01 questions linked to Fundamentals of Generative AI.
Practise AIF-C01 questions linked to Guidelines for Responsible AI.
Practise AIF-C01 questions linked to Security, Compliance and Governance for AI Solutions.
Practise AIF-C01 questions linked to AIF-C01 fundamentals.
Practise AIF-C01 questions linked to AIF-C01 scenario.
Practise AIF-C01 questions linked to AIF-C01 troubleshooting.
Practice this exam
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Applications of Foundation Models — This question tests Applications of Foundation Models — Read the scenario before looking for a memorised answer..
The correct answer is: max_tokens — The `max_tokens` parameter directly controls the maximum number of tokens (words or subwords) in the generated output. By decreasing this value, you explicitly cap the length of the marketing copy, making it less verbose. This is the most direct way to limit output length in Amazon Bedrock and other LLM APIs.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Keep practising
Last reviewed: Jun 25, 2026
This AIF-C01 practice question is part of Courseiva's free Amazon Web Services 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 AIF-C01 exam.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.