Generative AI Leader Fundamentals of Generative AI Practice Question
Which THREE of the following are common techniques to reduce harmful biases in generative AI models? (Choose three.)
⚠ Common exam trap
Google Cloud often tests the distinction between hyperparameter tuning (like temperature) and actual bias mitigation techniques, so candidates mistakenly think lowering temperature reduces bias when it only affects output randomness.
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
✓
Use reinforcement learning from human feedback (RLHF) with a reward model that penalizes biased or unfair outputs.
A is correct because RLHF uses a reward model trained on human preferences to score model outputs, and explicitly penalizing biased or unfair outputs during fine-tuning directly reduces harmful biases. This technique aligns the model's behavior with human values by optimizing against a learned reward signal that captures bias-related concerns.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use reinforcement learning from human feedback (RLHF) with a reward model that penalizes biased or unfair outputs.
Why this is correct
RLHF can shape model behavior to avoid biased generations.
- ✓
Curate diverse and balanced training datasets that overrepresent underrepresented groups.
Why this is correct
Balanced data reduces model bias toward majority groups.
- ✗
Decrease the model's temperature parameter to make outputs more deterministic.
Why it's wrong here
Temperature does not address bias; it affects randomness.
- ✓
Apply adversarial training to remove protected attribute information from hidden representations.
Why this is correct
Adversarial debiasing forces the model to not encode sensitive attributes.
- ✗
Conduct a legal review of all generated outputs before release.
Why it's wrong here
This is a process after deployment, not a technique to train the model.
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