- A
Decrease the top-p value
Why wrong: Decreasing top-p reduces the set of tokens considered, often making outputs less diverse.
- B
Increase the frequency penalty
Why wrong: Frequency penalty reduces repetition, which could help with creativity, but temperature directly impacts creativity more effectively.
- C
Decrease the max tokens limit
Why wrong: Decreasing max tokens makes the output shorter, which is opposite of what is desired.
- D
Increase the temperature parameter
Higher temperature (e.g., 0.8-1.0) makes the model take more risks, leading to more creative and varied outputs.
Question 960 of 1,000
AI0-001 AI Concepts and Techniques Practice Question
This AI0-001 practice question tests your understanding of ai concepts and techniques. 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 generative AI model is asked to 'Write a poem about AI' and returns a very short, generic response. The user wants longer, more creative outputs. Which parameter adjustment is MOST likely to help?
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
✓
Increase the temperature parameter
Increasing the temperature parameter raises the randomness of token selection, encouraging the model to explore less probable word sequences and produce more varied, creative, and longer outputs. A low temperature (e.g., 0.1) makes the model deterministic and repetitive, often yielding short, generic responses. By increasing temperature (e.g., to 0.8 or 1.0), the model is more likely to generate diverse and expansive text, directly addressing the user's request for longer, more creative poems.
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.
- ✗
Decrease the top-p value
Why it's wrong here
Decreasing top-p reduces the set of tokens considered, often making outputs less diverse.
- ✗
Increase the frequency penalty
Why it's wrong here
Frequency penalty reduces repetition, which could help with creativity, but temperature directly impacts creativity more effectively.
- ✗
Decrease the max tokens limit
Why it's wrong here
Decreasing max tokens makes the output shorter, which is opposite of what is desired.
- ✓
Increase the temperature parameter
Why this is correct
Higher temperature (e.g., 0.8-1.0) makes the model take more risks, leading to more creative and varied 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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that increasing max tokens (or decreasing it) is the primary way to control output length, when in fact temperature and top-p are the key parameters for influencing creativity and diversity, while max tokens simply sets a hard cutoff.
Trap categories for this question
Command / output trap
Decreasing top-p reduces the set of tokens considered, often making outputs less diverse.
Detailed technical explanation
How to think about this question
Temperature scales the logits (raw scores) before applying the softmax function: higher temperature (e.g., >1) flattens the probability distribution, making low-probability tokens more likely to be chosen, while lower temperature sharpens the distribution, favoring high-probability tokens. In practice, for creative writing tasks, a temperature between 0.7 and 1.0 is often used to balance coherence and creativity; values above 1.0 can lead to incoherent or nonsensical outputs. This parameter is critical in real-world applications like story generation or dialogue systems where output length and creativity must be dynamically controlled.
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 practitioner preparing for the AI0-001 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 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 AI0-001 question test?
AI Concepts and Techniques — This question tests AI Concepts and Techniques — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Increase the temperature parameter — Increasing the temperature parameter raises the randomness of token selection, encouraging the model to explore less probable word sequences and produce more varied, creative, and longer outputs. A low temperature (e.g., 0.1) makes the model deterministic and repetitive, often yielding short, generic responses. By increasing temperature (e.g., to 0.8 or 1.0), the model is more likely to generate diverse and expansive text, directly addressing the user's request for longer, more creative poems.
What should I do if I get this AI0-001 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.
About these practice questions
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Last reviewed: Jul 4, 2026
This AI0-001 practice question is part of Courseiva's free CompTIA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI0-001 exam.
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