The answer is maxTokens. This parameter directly controls the maximum number of tokens—words or subword units—the model can generate in a single response, so increasing it allows for longer output when a response is cut off mid-sentence. On the Oracle Cloud Infrastructure Generative AI Professional 1Z0-1127 exam, this question tests your understanding of inference parameters and their distinct roles; a common trap is confusing maxTokens with temperature or topP, which affect randomness and diversity, not length. Remember that temperature and topP shape how tokens are chosen, while maxTokens sets the hard ceiling on how many can be produced. For a quick memory tip: think of maxTokens as the “length limiter” and temperature as the “creativity dial”—if the output stops too soon, always check the token limit first.
1Z0-1127 Fundamentals of Large Language Models Practice Question
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.
Refer to the exhibit. A data scientist runs this inference request and receives a response that is incomplete and seems to stop mid-sentence. Which parameter should be adjusted to allow the model to generate longer outputs?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
maxTokens
Option B is correct because maxTokens directly limits the number of tokens generated; increasing it allows the model to produce longer responses. Option A (temperature) affects randomness, not length. Option C (topP) affects token selection diversity. Options D and E affect repetition penalties, not output length.
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.
✓
maxTokens
Why this is correct
maxTokens sets the maximum number of tokens to generate; increasing it yields longer outputs.
Related concept
Read the scenario before looking for a memorised answer.
✗
temperature
Why it's wrong here
Temperature controls randomness, not output length.
✗
topP
Why it's wrong here
topP controls nucleus sampling diversity, not length.
✗
frequencyPenalty
Why it's wrong here
Frequency penalty reduces repetition, does not extend length.
✗
presencePenalty
Why it's wrong here
Presence penalty discourages topic repetition, does not affect length.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Trap categories for this question
Command / output trap
Temperature controls randomness, not output length.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→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 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
Got this wrong? Here's your next step.
Identify which 1Z0-1127 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Fundamentals of Large Language Models — This question tests Fundamentals of Large Language Models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: maxTokens — Option B is correct because maxTokens directly limits the number of tokens generated; increasing it allows the model to produce longer responses. Option A (temperature) affects randomness, not length. Option C (topP) affects token selection diversity. Options D and E affect repetition penalties, not output length.
What should I do if I get this 1Z0-1127 question wrong?
Identify which 1Z0-1127 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A developer is using the OCI Generative AI API to generate text. The responses are often too short and incomplete. Which parameter adjustment is most likely to produce longer, more complete responses?
easy
A.Decrease the max_tokens parameter.
✓ B.Increase the max_tokens parameter.
C.Increase the top_p parameter.
D.Decrease the frequency_penalty parameter.
Why B: The max_tokens parameter controls the maximum number of tokens (words or subwords) the model can generate in a single response. By increasing max_tokens, the model is allowed to produce longer sequences, which directly addresses the issue of responses being too short and incomplete. In the OCI Generative AI API, this is the primary parameter for capping output length.
Last reviewed: Jun 23, 2026
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