Quick Answer
The answer is to increase the max_tokens parameter. This adjustment is correct because the max_tokens parameter directly controls the maximum length of the model’s output; when summarizing long documents, a low max_tokens value forces the model to truncate its response, cutting off key details before the summary is complete. By raising this limit, you give the Claude model enough room to generate a thorough, comprehensive summary that captures all critical points from the source text. On the AWS Certified AI Practitioner AIF-C01 exam, this question tests your understanding of how inference parameters affect output quality, often appearing as a scenario where a developer sees incomplete results and must identify the root cause. A common trap is confusing max_tokens with temperature or top_p, which control creativity rather than length. Remember the memory tip: “More tokens, more completeness—if your summary is cut short, max_tokens is your support.”
This AIF-C01 practice question tests your understanding of fundamentals of generative ai. 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 developer is using Amazon Bedrock's Claude model to summarize long documents. The developer notices that the summaries sometimes miss key points. Which parameter adjustment is most likely to improve summary completeness?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Increase the max_tokens parameter.
More tokens allow the model to include more details in the summary.
Increase the top_k parameter.
Why wrong: Top_k limits the number of tokens considered, not length.
Increase the temperature parameter.
Why wrong: Higher temperature makes output more random, not more complete.
Increase the top_p parameter.
Why wrong: Top_p controls nucleus sampling, not output 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
Increase the max_tokens parameter.
Increasing max_tokens allows the model to generate longer outputs, which is essential when summarizing long documents because the summary may need more tokens to capture all key points. If max_tokens is too low, the model truncates the response, potentially omitting important details. This directly addresses the issue of missing key points by providing sufficient output length for a complete summary.
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.
Increase the max_tokens parameter.
Why this is correct
More tokens allow the model to include more details in the summary.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Increase the top_k parameter.
Why it's wrong here
Top_k limits the number of tokens considered, not length.
Increase the temperature parameter.
Why it's wrong here
Higher temperature makes output more random, not more complete.
Increase the top_p parameter.
Why it's wrong here
Top_p controls nucleus sampling, not output length.
Common exam traps
Cisco often tests the misconception that parameters controlling randomness (temperature, top_k, top_p) affect output length or completeness, when in fact they only influence token selection diversity and creativity.
Trap categories for this question
Command / output trap
Higher temperature makes output more random, not more complete.
Detailed technical explanation
The max_tokens parameter sets the maximum number of tokens (subword units) the model can generate in a single response. For Claude models on Amazon Bedrock, the default max_tokens is often 256 or 512, which can be insufficient for summarizing lengthy documents. By increasing max_tokens to 2048 or higher, the model has more capacity to produce a comprehensive summary. However, note that the model's context window (e.g., 100K tokens for Claude 2.1) also limits the input document length, so both input and output token limits must be considered.
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
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FAQ
Fundamentals of Generative AI — This question tests Fundamentals of Generative AI — Read the scenario before looking for a memorised answer..
The correct answer is: Increase the max_tokens parameter. — Increasing max_tokens allows the model to generate longer outputs, which is essential when summarizing long documents because the summary may need more tokens to capture all key points. If max_tokens is too low, the model truncates the response, potentially omitting important details. This directly addresses the issue of missing key points by providing sufficient output length for a complete summary.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 25, 2026
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