Fundamentals of Large Language Models →mediumMultiple SelectObjective-mapped

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

Which three factors most significantly affect the quality of an LLM's output? (Select THREE)

⚠ Common exam trap

Oracle often tests the misconception that hardware resources like GPU count directly improve output quality, whereas in reality they only affect performance metrics like latency and throughput, not the semantic quality of the generated text.

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

✓

Clarity of the prompt

The clarity of the prompt directly determines how well the LLM interprets the user's intent. A well-structured, unambiguous prompt reduces ambiguity and guides the model toward generating relevant and coherent responses, while a vague or poorly worded prompt often leads to off-target or nonsensical output.

Answer analysis

Option-by-option breakdown

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

✗
Model's context window size
Why it's wrong here
Incorrect: While important, it doesn't directly affect quality per se.
✓
Clarity of the prompt
Why this is correct
Correct: Clear prompts yield more accurate responses.
✗
Number of GPUs used during inference
Why it's wrong here
Incorrect: GPU count affects speed, not output quality.
✓
Temperature setting
Why this is correct
Correct: Temperature controls randomness and creativity.
✓
Quality of training data
Why this is correct
Correct: High-quality data leads to better output.

Go deeper

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

Why each option matters

Option-by-option breakdown

Related to this question