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AIF-C01 Applications of Foundation Models Practice Question

A data scientist uses Amazon Bedrock. The model responses are too long. Which parameter should they adjust to limit the output length?

⚠ Common exam trap

AWS often tests the distinction between parameters that control output length (`max_tokens`) versus those that control output randomness or diversity (`temperature`, `top_p`), leading candidates to confuse 'limiting length' with 'limiting creativity'.

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

max_tokens

The `max_tokens` parameter directly controls the maximum number of tokens (words or subwords) the model can generate in a single response. By reducing this value, the data scientist caps the output length, preventing overly long responses. Temperature and top_p affect randomness and diversity, not length, while stop sequences define when generation halts but do not enforce a hard token limit.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • temperature

    Why it's wrong here

    Temperature affects randomness, not length.

  • max_tokens

    Why this is correct

    Reducing max_tokens directly caps the output length.

  • stop sequences

    Why it's wrong here

    Stop sequences terminate generation but do not limit length by themselves.

  • top_p

    Why it's wrong here

    top_p controls nucleus sampling, not length.

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Written by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

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.