- A
Randomly show luxury recommendations to a subset of users regardless of their behavior
Why wrong: Random shows would reduce relevance and customer satisfaction.
- B
Remove zip code and any income-correlated features from the training data
Removing biased features eliminates the source of unfairness in recommendations.
- C
Add more training data from less affluent areas to balance the dataset
Why wrong: Adding data may help but the model may still learn biased patterns from zip code.
- D
Implement a separate recommendation pipeline for luxury items based only on search history
Why wrong: This does not address the underlying bias; zip code may still influence via other features.
AIF-C01 Guidelines for Responsible AI Practice Question
This AIF-C01 practice question tests your understanding of guidelines for responsible ai. 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 large e-commerce company uses a recommendation system to suggest products to customers. Recently, a data scientist noticed that the model's recommendations for high-value luxury items are predominantly shown to users in affluent zip codes, while users in less affluent areas rarely see these items, even if they have searched for them. The company is concerned about fairness and wants to ensure all customers have equal access to recommendations regardless of location. The current model uses collaborative filtering on historical purchase data. The team needs to modify the system without sacrificing overall recommendation accuracy. Which action best addresses the fairness concern while maintaining performance?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"best"Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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
Remove zip code and any income-correlated features from the training data
The most effective approach is to ensure the model does not use zip code or any feature correlated with income as a direct or indirect input. This removes the proxy for socioeconomic status. Simply equalizing recommendation frequency artificially may hurt relevance. Personalizing based on search history is already being done but zip code bias remains. Adding more training data may not help if the bias is in the features.
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.
- ✗
Randomly show luxury recommendations to a subset of users regardless of their behavior
Why it's wrong here
Random shows would reduce relevance and customer satisfaction.
- ✓
Remove zip code and any income-correlated features from the training data
Why this is correct
Removing biased features eliminates the source of unfairness in recommendations.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
OSPF neighbours must agree on key parameters.
- ✗
Add more training data from less affluent areas to balance the dataset
Why it's wrong here
Adding data may help but the model may still learn biased patterns from zip code.
- ✗
Implement a separate recommendation pipeline for luxury items based only on search history
Why it's wrong here
This does not address the underlying bias; zip code may still influence via other features.
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.
Trap categories for this question
Command / output trap
Random shows would reduce relevance and customer satisfaction.
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 company's IT admin needs to give a contractor read-only access to production logs without sharing account credentials. Using role-based access control (RBAC) and temporary scoped permissions — not a permanent shared password — is the correct pattern. Questions like this test whether you can apply least-privilege access across cloud identity services.
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 AIF-C01 OSPF questions on adjacency and route selection.
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Guidelines for Responsible AI — study guide chapter
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Guidelines for Responsible AI practice questions
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FAQ
Questions learners often ask
What does this AIF-C01 question test?
Guidelines for Responsible AI — This question tests Guidelines for Responsible AI — OSPF neighbours must agree on key parameters..
What is the correct answer to this question?
The correct answer is: Remove zip code and any income-correlated features from the training data — The most effective approach is to ensure the model does not use zip code or any feature correlated with income as a direct or indirect input. This removes the proxy for socioeconomic status. Simply equalizing recommendation frequency artificially may hurt relevance. Personalizing based on search history is already being done but zip code bias remains. Adding more training data may not help if the bias is in the features.
What should I do if I get this AIF-C01 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 AIF-C01 OSPF questions on adjacency and route selection.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
What is the key concept behind this question?
OSPF neighbours must agree on key parameters.
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Last reviewed: Jun 23, 2026
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