- A
Remove the zip code feature from the model inputs.
Why wrong: Removing zip code may not eliminate bias if other features (e.g., income, property value) correlate with it.
- B
Retrain the model with a balanced dataset that includes more examples from underrepresented neighborhoods and enforce fairness constraints.
Balanced data reduces bias and fairness constraints ensure equitable treatment, aligning with ethical AI principles.
- C
Adjust the approval threshold lower only for applicants from low-income neighborhoods.
Why wrong: Different thresholds for different groups could be seen as discriminatory and may violate equal lending laws.
- D
Continue using the existing model since it has high accuracy and reduces defaults.
Why wrong: High accuracy does not guarantee fairness; ignoring disparity can lead to regulatory penalties and reputational damage.
AI Associate Ethical Considerations of AI Practice Question
This AI Associate practice question tests your understanding of ethical considerations of ai. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 financial services firm deployed an AI model to automate loan approvals. The model was trained on historical loan data from the past 10 years, which shows that applicants from certain zip codes have higher default rates. After six months, the company's compliance team receives complaints that applicants from predominantly low-income neighborhoods are being rejected at a much higher rate than applicants from affluent areas, even when their financial profiles are similar. The model's overall accuracy remains high (95%), and the loan default rate has decreased by 15% since deployment. The company wants to address the ethical concerns without sacrificing performance. Which course of action should the company take?
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
Retrain the model with a balanced dataset that includes more examples from underrepresented neighborhoods and enforce fairness constraints.
Option B is correct because retraining with balanced data mitigates the representation bias, addressing the root cause. Option A ignores the fairness issue. Option C removes a feature that may be a proxy for other factors, but it may not eliminate bias if other correlated features remain. Option D adjusts thresholds only for some groups, which could be considered unfair and may not be accepted by regulators.
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.
- ✗
Remove the zip code feature from the model inputs.
Why it's wrong here
Removing zip code may not eliminate bias if other features (e.g., income, property value) correlate with it.
- ✓
Retrain the model with a balanced dataset that includes more examples from underrepresented neighborhoods and enforce fairness constraints.
Why this is correct
Balanced data reduces bias and fairness constraints ensure equitable treatment, aligning with ethical AI principles.
Related concept
OSPF neighbours must agree on key parameters.
- ✗
Adjust the approval threshold lower only for applicants from low-income neighborhoods.
Why it's wrong here
Different thresholds for different groups could be seen as discriminatory and may violate equal lending laws.
- ✗
Continue using the existing model since it has high accuracy and reduces defaults.
Why it's wrong here
High accuracy does not guarantee fairness; ignoring disparity can lead to regulatory penalties and reputational damage.
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 network engineer at a university connects two campus buildings via a fibre link. Both routers run OSPF, but no adjacency forms — even though both routers can ping each other. The engineer finds one router is in area 0 and the other in area 1. OSPF adjacency requires matching area numbers, hello/dead timers, and network type. IP reachability alone is not enough.
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 AI Associate OSPF questions on adjacency and route selection.
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FAQ
Questions learners often ask
What does this AI Associate question test?
Ethical Considerations of AI — This question tests Ethical Considerations of AI — OSPF neighbours must agree on key parameters..
What is the correct answer to this question?
The correct answer is: Retrain the model with a balanced dataset that includes more examples from underrepresented neighborhoods and enforce fairness constraints. — Option B is correct because retraining with balanced data mitigates the representation bias, addressing the root cause. Option A ignores the fairness issue. Option C removes a feature that may be a proxy for other factors, but it may not eliminate bias if other correlated features remain. Option D adjusts thresholds only for some groups, which could be considered unfair and may not be accepted by regulators.
What should I do if I get this AI Associate 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 AI Associate 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: Jun 23, 2026
This AI Associate practice question is part of Courseiva's free Salesforce 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 AI Associate exam.
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