Generative AI Leader Practice Question: Techniques to Improve Generative AI Model Output
Which TWO techniques are effective for reducing bias in generative AI model outputs?
⚠ Common exam trap
Google Cloud often tests the misconception that increasing model size or adding post-hoc filters is sufficient to mitigate bias, when in reality these approaches fail to address the root causes of bias in training data and model representations.
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
✓
Training on diverse and representative datasets
Training on diverse and representative datasets directly reduces sampling bias and coverage gaps in the training distribution, which are primary sources of stereotypical or skewed outputs. By ensuring the model sees balanced examples across demographics, contexts, and edge cases, it learns more equitable representations and reduces the likelihood of generating biased content.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increasing model size to learn more patterns
Why it's wrong here
Larger models may amplify rather than reduce bias.
- ✓
Training on diverse and representative datasets
Why this is correct
Correct: Diverse data helps reduce biased associations.
- ✗
Relying solely on post-hoc filters
Why it's wrong here
Post-hoc filters are insufficient and may not catch subtle biases.
- ✓
Using adversarial debiasing methods during fine-tuning
Why this is correct
Correct: Adversarial methods penalize biased predictions.
- ✗
Limiting the model to only factual prompts
Why it's wrong here
This restricts input but does not address inherent bias in training.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This Generative AI Leader practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the Generative AI Leader exam.