Fundamentals of Large Language Models →mediumMultiple SelectObjective-mapped

1Z0-1127-25 Fundamentals of Large Language Models Practice Question

Which TWO factors most directly impact the consistency of text generated by an LLM when the same prompt is used multiple times?

⚠ Common exam trap

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.

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

✓

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.

Answer analysis

Option-by-option breakdown

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.
✗
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.
✗
Seed
Why it's wrong here
Seed is not typically settable in OCI GenAI; without it, consistency is not controlled.

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1Z0-1127-25 Fundamentals of Large Language Models Practice Question

Why each option matters

Option-by-option breakdown

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