Fundamentals of Large Language Models →easyMultiple SelectObjective-mapped

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

Which TWO techniques can help reduce bias in LLM outputs?

⚠ Common exam trap

Oracle often tests the misconception that lowering temperature or increasing model size can fix bias, when in reality these parameters affect randomness and capacity, not the underlying distributional fairness of the training data.

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

✓

Using diverse training data

Using diverse training data helps the model learn from a wide range of perspectives, reducing the risk of over-representing any single group or viewpoint. This directly mitigates bias by ensuring the training distribution is more representative of the real world, rather than skewed toward a dominant demographic or cultural norm.

Answer analysis

Option-by-option breakdown

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

✗
Setting temperature to 0
Why it's wrong here
Temperature 0 makes output deterministic but does not address bias.
✗
Using only English data
Why it's wrong here
Limiting to one language increases cultural bias.
✓
Using diverse training data
Why this is correct
Diverse data reduces representation bias.
✗
Increasing model size
Why it's wrong here
Larger models may capture more biases.
✓
Applying adversarial debiasing
Why this is correct
Adversarial debiasing actively reduces bias.

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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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