Quick Answer
The answer is Temperature and Top_p, as these two parameters most directly impact the consistency of text generated by an LLM when the same prompt is used multiple times. Temperature controls the randomness of token selection by scaling the probability distribution—a lower value (e.g., 0.1) makes the model more deterministic by favoring high-probability tokens, while a higher value increases variability. Top_p, or nucleus sampling, further refines consistency by setting a cumulative probability threshold; a lower Top_p (e.g., 0.1) restricts token selection to only the most likely candidates, reducing surprise across repeated runs. On the Oracle Cloud Infrastructure Generative AI Professional 1Z0-1127 exam, this concept tests your understanding of inference parameters that govern output reproducibility, often appearing in scenario-based questions where you must choose settings for stable or creative generation. A common trap is confusing Top_k (which limits token count) with Top_p (which limits probability mass). Memory tip: think of Temperature as the “creativity dial” and Top_p as the “focus filter”—low on both gives you a reliable, repeatable answer.
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.
Which TWO factors most directly impact the consistency of text generated by an LLM when the same prompt is used multiple times?
Top_p
Top_p controls the nucleus of tokens considered; lower values make output more focused.
Batch size
Why wrong: Batch size affects throughput, not output consistency.
Max_tokens
Why wrong: Max_tokens limits output length, not variability.
Temperature
Higher temperature increases randomness, lower makes output more deterministic.
Seed
Why wrong: Seed is not typically settable in OCI GenAI; without it, consistency is not controlled.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Top_p
Top_p (nucleus sampling) directly impacts consistency by controlling the cumulative probability threshold for token selection. A lower Top_p (e.g., 0.1) restricts the model to only the most probable tokens, reducing randomness and making outputs more deterministic across repeated prompts. This parameter, along with Temperature, is a primary lever for managing output variability in LLMs.
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.
Top_p
Why this is correct
Top_p controls the nucleus of tokens considered; lower values make output more focused.
Related concept
Read the scenario before looking for a memorised answer.
Batch size
Why it's wrong here
Batch size affects throughput, not output consistency.
Max_tokens
Why it's wrong here
Max_tokens limits output length, not variability.
Temperature
Why this is correct
Higher temperature increases randomness, lower makes output more deterministic.
Related concept
Read the scenario before looking for a memorised answer.
Seed
Why it's wrong here
Seed is not typically settable in OCI GenAI; without it, consistency is not controlled.
Common exam traps
Oracle often tests the distinction between inference-time parameters (Temperature, Top_p) and training/hardware parameters (Batch size), or between parameters that control randomness (Temperature, Top_p) versus those that control output length (Max_tokens), leading candidates to mistakenly select Seed as a primary consistency factor.
Trap categories for this question
Command / output trap
Batch size affects throughput, not output consistency.
Detailed technical explanation
Under the hood, Temperature scales the logits before softmax, with lower values (e.g., 0.1) sharpening the probability distribution to favor high-probability tokens, while Top_p dynamically selects the smallest set of tokens whose cumulative probability exceeds the threshold. In practice, setting Temperature to 0 (greedy decoding) and Top_p to 1 (no filtering) yields fully deterministic output, but many APIs (e.g., OpenAI) require Temperature > 0 for Top_p to have effect. A real-world scenario: for a customer service chatbot that must give identical answers to the same query, you would set Temperature to 0 and Top_p to 1, ignoring Seed entirely.
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: Top_p — Top_p (nucleus sampling) directly impacts consistency by controlling the cumulative probability threshold for token selection. A lower Top_p (e.g., 0.1) restricts the model to only the most probable tokens, reducing randomness and making outputs more deterministic across repeated prompts. This parameter, along with Temperature, is a primary lever for managing output variability in LLMs.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
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