Quick Answer
The answer is to increase maxTokens, as this parameter directly controls output length and is the most effective adjustment for ensuring complete summaries. When a model truncates generation due to a low token limit, it may omit critical clauses from lengthy legal documents, so raising maxTokens allows the model to produce a longer, more thorough summary that covers all essential sections. On the Oracle Cloud Infrastructure Generative AI Professional 1Z0-1127 exam, this question tests your understanding of how maxTokens differs from parameters like temperature or topP, which affect randomness or vocabulary selection rather than output length. A common trap is confusing maxTokens with frequency penalty or stop sequences, but remember: if the output is too short, maxTokens is the knob to turn. Memory tip: “Max tokens = max content” — when you need the model to say more, give it more tokens to spend.
This 1Z0-1127 practice question tests your understanding of fundamentals of large language 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 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?
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.
Adjust frequencyPenalty.
Why wrong: Frequency penalty reduces repetition but does not directly affect completeness.
Increase temperature.
Why wrong: Higher temperature increases randomness, not completeness.
Decrease topP.
Why wrong: Decreasing topP reduces diversity, which may exacerbate omissions by narrowing token selection.
Increase maxTokens.
A larger token limit enables longer summaries, helping to include critical clauses.
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 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.
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.
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.
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.
Common exam traps
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.
Detailed technical explanation
Under the hood, the maxTokens parameter sets an absolute cap on the number of tokens (words/subwords) the model can generate in a single response. In OCI Generative AI, the summarization model uses a transformer decoder that stops generation once it reaches this limit or an end-of-sequence token. If the source document is long, the model may allocate tokens to introductory or repetitive content first, leaving insufficient capacity for later critical clauses. A real-world scenario: summarizing a 50-page contract with maxTokens=200 might produce a fluent but incomplete summary, while increasing to 500 tokens allows coverage of termination clauses and liability caps.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A practitioner preparing for the 1Z0-1127 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
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FAQ
Fundamentals of Large Language Models — This question tests Fundamentals of Large Language Models — Read the scenario before looking for a memorised answer..
The correct answer is: 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.
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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Same concept, more angles
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Variation 1. Refer to the exhibit. The output is very short and cuts off mid-sentence. Which parameter is most likely the cause?
hardWhy A: The 'max-tokens' parameter limits the number of tokens in the generated response. Setting it to 500, while typically sufficient, might still cause truncation if the model's context window is nearly full or if the prompt is long. However, among options, 'max-tokens' is the direct control for output length. Option C is correct.
Last reviewed: Jun 30, 2026
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