- A
Model perplexity on held-out data
Why wrong: Perplexity measures language model fluency, not fairness.
- B
Equalized odds across demographic groups
Equalized odds checks for fairness by comparing error rates across groups.
- C
Overall accuracy on the test set
Why wrong: Overall accuracy can mask disparities; high accuracy might still hide bias.
- D
Area under the ROC curve (AUC)
Why wrong: AUC measures overall ranking quality, not group-specific disparities.
Question 941 of 997
Generative AI Leader Responsible AI and Data Governance Practice Question
This Generative AI Leader practice question tests your understanding of responsible ai and data governance. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A machine learning engineer is evaluating a generative AI model for bias. They have a diverse test set covering gender, race, and age groups. Which metric would best indicate if the model's performance is systematically worse for certain demographic groups?
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
✓
Equalized odds across demographic groups
Equalized odds measures whether a model's predictions have equal false positive/negative rates across groups. The other options either measure different aspects or are not specific to fairness.
Key principle: OSPF neighbour adjacency depends on matching area, hello/dead timers, network type, and authentication — IP reachability alone is not enough.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Model perplexity on held-out data
Why it's wrong here
Perplexity measures language model fluency, not fairness.
- ✓
Equalized odds across demographic groups
Why this is correct
Equalized odds checks for fairness by comparing error rates across groups.
Related concept
OSPF neighbours must agree on key parameters.
- ✗
Overall accuracy on the test set
Why it's wrong here
Overall accuracy can mask disparities; high accuracy might still hide bias.
- ✗
Area under the ROC curve (AUC)
Why it's wrong here
AUC measures overall ranking quality, not group-specific disparities.
Common exam traps
Common exam trap: OSPF can fail even when IP connectivity looks correct
OSPF neighbour formation depends on matching areas, timers, network type, authentication and passive-interface behaviour. Do not choose an answer only because the devices can ping.
Detailed technical explanation
How to think about this question
OSPF questions usually test the details that control adjacency and route selection. Read the neighbour state, area, router ID and interface configuration before deciding what is wrong.
KKey Concepts to Remember
- OSPF neighbours must agree on key parameters.
- Router ID selection can affect neighbour relationships and LSDB output.
- OSPF cost influences the preferred path.
- A route can appear in OSPF information but not become the installed route.
TExam Day Tips
- Check area mismatch first when OSPF adjacency fails.
- Review passive interfaces when a network is advertised but no neighbour forms.
- Use show ip ospf neighbor and show ip route clues carefully.
Key takeaway
OSPF neighbour adjacency depends on matching area, hello/dead timers, network type, and authentication — IP reachability alone is not enough.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. OSPF neighbour adjacency depends on matching area, hello/dead timers, network type, and authentication — IP reachability alone is not enough. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Review OSPF neighbour requirements — matching area type, hello and dead timers, network type, stub flags, and authentication. Study show ip ospf neighbor states (INIT, 2-WAY, FULL). Then practise related Generative AI Leader OSPF questions on adjacency and route selection.
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Responsible AI and Data Governance — study guide chapter
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Responsible AI and Data Governance practice questions
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FAQ
Questions learners often ask
What does this Generative AI Leader question test?
Responsible AI and Data Governance — This question tests Responsible AI and Data Governance — OSPF neighbours must agree on key parameters..
What is the correct answer to this question?
The correct answer is: Equalized odds across demographic groups — Equalized odds measures whether a model's predictions have equal false positive/negative rates across groups. The other options either measure different aspects or are not specific to fairness.
What should I do if I get this Generative AI Leader question wrong?
Review OSPF neighbour requirements — matching area type, hello and dead timers, network type, stub flags, and authentication. Study show ip ospf neighbor states (INIT, 2-WAY, FULL). Then practise related Generative AI Leader OSPF questions on adjacency and route selection.
What is the key concept behind this question?
OSPF neighbours must agree on key parameters.
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Last reviewed: Jul 4, 2026
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.
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