mediumMultiple Choice
Generative AI Leader Practice Question: Using a generative AI model to screen job…
A company is using a generative AI model to screen job applications. They want to ensure compliance with Google's AI Principle of avoiding unfair bias. Which practice is most effective in mitigating bias during the screening process?
⚠ Common exam trap
Google often tests the misconception that removing demographic features is sufficient to eliminate bias, but the trap here is that proxy variables and latent correlations in the data can still cause the model to discriminate indirectly.
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
✓
Audit the training data for demographic representativeness and evaluate the model using fairness metrics
Auditing training data for demographic representativeness and evaluating the model using fairness metrics directly addresses the root causes of bias in generative AI systems. This practice aligns with Google's AI Principle of avoiding unfair bias by proactively identifying and mitigating imbalances in the data and measuring the model's performance across demographic groups using metrics like demographic parity or equal opportunity.
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 a pre-trained model without modification
Why it's wrong here
An unmodified pre-trained model carries whatever historical biases its training corpus encoded, and screening decisions inherit them unchecked. It tempts because it needs no extra work and performs well generally, but bias mitigation requires evaluation, debiasing or fairness-constrained tuning on the actual screening task and applicant population.
- ✗
Remove all demographic information from the resumes before processing
Why it's wrong here
Stripping demographics does not remove bias: correlated proxies such as postcodes, university names, employment gaps and phrasing still encode protected attributes, and the model can discriminate through them. It tempts as an intuitive anonymisation step, but effective mitigation requires measuring outcomes across groups and testing the model itself.
- ✓
Audit the training data for demographic representativeness and evaluate the model using fairness metrics
Why this is correct
Auditing training data exposes under-representation that skews outputs, while fairness metrics such as demographic parity quantify disparate impact across protected groups. This directly satisfies the principle of avoiding unfair bias, since bias originates in data and must be measured, not assumed absent.
- ✗
Only allow human reviewers to see the top 10% of candidates
Why it's wrong here
Restricting human review to the top 10% leaves the model's biased ranking unchallenged for the other 90%, who are rejected automatically. It tempts because human oversight sounds like a safeguard, but mitigation requires reviewing the model's decisions and outcomes across the whole applicant pool, not just its highest-scoring outputs.
Quick reference
RAID Level Comparison
| RAID Level | Min Disks | Fault Tolerance | Read | Write | Usable Capacity |
|---|---|---|---|---|---|
| RAID 0 | 2 | None | Excellent | Excellent | 100% |
| RAID 1 | 2 | 1 disk | Good | Moderate | 50% |
| RAID 5 | 3 | 1 disk | Good | Moderate | 67–94% |
| RAID 6 | 4 | 2 disks | Good | Lower | 50–88% |
| RAID 10 | 4 | 1 disk per mirror | Excellent | Good | 50% |
RAID is not a backup strategy — it protects against disk failure but not against accidental deletion, ransomware, or site-level events.
Go deeper
Related to this question
About these practice questions
This Generative AI Leader question is part of Courseiva's 1,008-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
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.