Fundamentals of Large Language Models →mediumMultiple SelectObjective-mapped

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

Which TWO factors are most likely to cause hallucinations in LLMs?

⚠ Common exam trap

Oracle often tests the misconception that low top-p or short context windows are primary causes of hallucinations, when in fact high temperature and insufficient training data are the two most direct factors that increase the likelihood of generating false or fabricated content.

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

✓

High temperature

A high temperature setting increases the randomness of token sampling, making the model more likely to generate plausible-sounding but factually incorrect or nonsensical outputs. This directly contributes to hallucinations by encouraging the model to deviate from the most probable, grounded responses.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✓
High temperature
Why this is correct
High temperature increases randomness, leading to less factual outputs.
✗
Short context window
Why it's wrong here
Short context may cause incompleteness but not necessarily hallucination.
✗
Excessive fine-tuning
Why it's wrong here
Excessive fine-tuning may cause overfitting but not directly hallucinations.
✗
Low top-p
Why it's wrong here
Low top-p reduces diversity, making outputs less creative but not more hallucinatory.
✓
Inadequate training data
Why this is correct
Lack of relevant data can cause the model to guess or hallucinate.

Go deeper

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

Why each option matters

Option-by-option breakdown

Related to this question