Fundamentals of Large Language Models →hardMultiple ChoiceObjective-mapped

1Z0-1127-25 Fundamentals of Large Language Models Practice Question

A team uses OCI Generative AI's summarization feature to condense legal documents. The summaries sometimes omit critical clauses. Which parameter adjustment is most likely to improve completeness?

⚠ Common exam trap

Oracle often tests the misconception that randomness parameters (temperature, topP) or repetition penalties control output length, when in fact only maxTokens directly determines how much text the model can produce.

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

✓

Increase maxTokens.

Increasing maxTokens (option D) is the most direct way to improve completeness because it extends the maximum length of the generated summary, allowing the model to include more content from the source legal document. Critical clauses are often omitted when the token limit truncates the output before the model can cover all essential sections. This parameter controls the output length, not the style or randomness of the generation.

Answer analysis

Option-by-option breakdown

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

✗
Adjust frequencyPenalty.
Why it's wrong here
Frequency penalty reduces repetition but does not directly affect completeness.
✗
Increase temperature.
Why it's wrong here
Higher temperature increases randomness, not completeness.
✗
Decrease topP.
Why it's wrong here
Decreasing topP reduces diversity, which may exacerbate omissions by narrowing token selection.
✓
Increase maxTokens.
Why this is correct
A larger token limit enables longer summaries, helping to include critical clauses.

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1Z0-1127-25 Fundamentals of Large Language Models Practice Question

Why each option matters

Option-by-option breakdown

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