The answer is to increase top_p from 0.9 to 0.95. This adjustment expands the nucleus of probability mass considered during sampling, allowing the model to select from a broader set of plausible next tokens, which directly increases output diversity while still maintaining relevance to the product catalog. In the context of the Oracle Cloud Infrastructure Generative AI Professional 1Z0-1127 exam, this question tests your understanding of how sampling parameters like top_p control the trade-off between creativity and coherence. A common trap is to assume that lowering temperature is the only way to reduce repetitiveness, but here temperature is already moderate at 0.8, and increasing top_p is more effective because it widens the token pool without introducing randomness that could harm relevance. Remember the memory tip: “Top-P expands the pool, temperature controls the cool”—so when you need more variety without losing focus, widen the nucleus 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.
An e-commerce company fine-tuned a Cohere Command model on their product catalog to generate product descriptions. During inference, they notice the model outputs are too repetitive: it often repeats similar phrases across different products, and the descriptions lack diversity. The team wants to increase the variety of the generated text without sacrificing relevance. They are currently using temperature=0.8, top_p=0.9, frequency_penalty=0, and presence_penalty=0. Which parameter adjustment should they make to most effectively increase diversity?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Increase top_p from 0.9 to 0.95.
Increasing top_p from 0.9 to 0.95 expands the nucleus of tokens considered during sampling, allowing the model to select from a wider set of plausible next tokens. This directly increases output diversity while still maintaining relevance, as tokens outside the top 90% probability mass are now included. The current settings already have moderate temperature and no penalties, so broadening top_p is the most effective single adjustment to reduce repetitiveness.
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 temperature from 0.8 to 0.5.
Why it's wrong here
Lower temperature makes the model more deterministic, reducing diversity.
✗
Set frequency_penalty to a negative value (e.g., -0.5).
Why it's wrong here
Negative penalty encourages repetition, which is the opposite of the desired effect.
✗
Increase max_tokens from 200 to 500.
Why it's wrong here
Max_tokens controls output length, not diversity.
✓
Increase top_p from 0.9 to 0.95.
Why this is correct
Higher top_p includes more tokens in the sampling pool, increasing diversity.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Oracle often tests the misconception that increasing temperature always increases diversity, when in fact decreasing temperature reduces randomness, and the most effective lever for diversity in a fine-tuned model is often adjusting top-p or adding a positive frequency penalty.
Trap categories for this question
Command / output trap
Max_tokens controls output length, not diversity.
Detailed technical explanation
How to think about this question
Top-p (nucleus) sampling works by dynamically selecting the smallest set of tokens whose cumulative probability exceeds the threshold p, then redistributing probability mass among them. At p=0.9, only the top 90% of the probability distribution is considered; at p=0.95, more low-probability tokens become eligible, increasing lexical diversity. This is particularly effective for fine-tuned models like Cohere Command, which may have narrowed probability distributions due to domain-specific training, leading to repetitive outputs when top-p is too restrictive.
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 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 exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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: Increase top_p from 0.9 to 0.95. — Increasing top_p from 0.9 to 0.95 expands the nucleus of tokens considered during sampling, allowing the model to select from a wider set of plausible next tokens. This directly increases output diversity while still maintaining relevance, as tokens outside the top 90% probability mass are now included. The current settings already have moderate temperature and no penalties, so broadening top_p is the most effective single adjustment to reduce repetitiveness.
What should I do if I get this 1Z0-1127 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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. Which THREE techniques are commonly used to improve the quality of text generation?
hard
✓ A.Temperature scaling
✓ B.Top-k sampling
C.Greedy decoding
D.Random sampling
✓ E.Beam search
Why A: Temperature scaling is correct because it controls the randomness of token probability distributions by dividing logits before softmax; lower temperatures (e.g., 0.1) make the model more deterministic, while higher temperatures (e.g., 1.5) increase diversity. This directly influences the quality of generated text by balancing coherence and creativity.
Last reviewed: Jun 30, 2026
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