- A
Increase the top_k parameter
Why wrong: top_k influences vocabulary sampling but does not directly address formatting.
- B
Decrease the temperature parameter
Lower temperature reduces randomness, producing more deterministic and well-formed outputs.
- C
Increase the max_tokens parameter
Why wrong: max_tokens limits output length but does not affect formatting adherence.
- D
Add a stop sequence of '}'
Why wrong: Stop sequences terminate generation but do not prevent malformed JSON.
Question 59 of 1,000
AIF-C01 Generative AI and Foundation Models Practice Question
This AIF-C01 practice question tests your understanding of generative ai and 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 practitioner is using Amazon Bedrock to invoke Anthropic Claude for a text generation task. They need the model to output a JSON object with specific keys, and they have observed that the model occasionally produces malformed JSON. Which parameter adjustment is MOST likely to improve JSON formatting consistency?
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.
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
✓
Decrease the temperature parameter
Decreasing the temperature parameter reduces the randomness of the model's output, making it more deterministic and less likely to deviate from the expected JSON structure. Lower temperature values (e.g., 0.1–0.3) encourage the model to choose higher-probability tokens, which improves formatting consistency for structured outputs like JSON.
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.
- ✗
Increase the top_k parameter
Why it's wrong here
top_k influences vocabulary sampling but does not directly address formatting.
- ✓
Decrease the temperature parameter
Why this is correct
Lower temperature reduces randomness, producing more deterministic and well-formed outputs.
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 max_tokens parameter
Why it's wrong here
max_tokens limits output length but does not affect formatting adherence.
- ✗
Add a stop sequence of '}'
Why it's wrong here
Stop sequences terminate generation but do not prevent malformed JSON.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the misconception that increasing randomness parameters (top_k, temperature) improves output quality, when in fact reducing randomness is the key to enforcing strict formatting rules like JSON syntax.
Trap categories for this question
Command / output trap
max_tokens limits output length but does not affect formatting adherence.
Detailed technical explanation
How to think about this question
Temperature scales the logits (raw scores) before applying the softmax function; a lower temperature sharpens the probability distribution, making high-probability tokens even more likely and low-probability tokens less likely. For JSON generation, this reduces the chance of the model 'creatively' inserting stray characters or omitting required commas/colons. In practice, combining a low temperature (e.g., 0.2) with a structured prompt that includes a JSON schema example yields the most reliable structured output from Anthropic Claude on Bedrock.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this AIF-C01 question test?
Generative AI and Foundation Models — This question tests Generative AI and Foundation Models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Decrease the temperature parameter — Decreasing the temperature parameter reduces the randomness of the model's output, making it more deterministic and less likely to deviate from the expected JSON structure. Lower temperature values (e.g., 0.1–0.3) encourage the model to choose higher-probability tokens, which improves formatting consistency for structured outputs like JSON.
What should I do if I get this AIF-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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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