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Generative AI Leader Practice Question: A data scientist observes that a text generation…
A data scientist observes that a text generation model consistently produces outputs that stereotype certain genders. According to Google's AI Principles, what is the BEST first step?
⚠ Common exam trap
The Generative AI Leader exam often tests the misconception that mitigation (like fine-tuning or disclaimers) should be the immediate response, rather than the correct first step of systematic evaluation and measurement of bias.
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
✓
Evaluate the model's bias using a diverse test set across genders
Google's AI Principles emphasize that the first step in addressing bias is to evaluate and measure it using appropriate tools and diverse datasets. This aligns with Principle #2: 'Avoid creating or reinforcing unfair bias,' which requires testing models across relevant demographic groups before taking corrective action. Without evaluation, any subsequent mitigation steps would lack a baseline and could be ineffective or counterproductive.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Evaluate the model's bias using a diverse test set across genders
Why this is correct
Measuring bias with a diverse test set across genders establishes evidence of the stereotyping before any remediation. This satisfies the principle of avoiding unfair bias by first quantifying disparate outputs, so subsequent mitigation targets the actual observed skew rather than assumptions.
- ✗
Immediately stop using the model and delete it
Why it's wrong here
Deleting the model removes the system but skips the Principles' requirement to investigate and mitigate harm first, and abandons any beneficial use. It tempts as a decisive harm-avoidance response, which would fit only if the model could not be made safe after documented assessment and remediation attempts.
- ✗
Fine-tune the model on a gender-balanced dataset
Why it's wrong here
Fine-tuning may help but without evaluation, you cannot confirm the bias or measure improvement.
- ✗
Add a disclaimer that the model may exhibit bias
Why it's wrong here
A disclaimer shifts disclosure to users without removing the stereotyping output, so the harm persists. It tempts because transparency is a genuine Principle, but it applies alongside, not instead of, actively preventing unfair bias; a label alone does not satisfy that obligation.
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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.