- A
Protecting customer data privacy during model training and inference
Data privacy is a core ethical and legal requirement.
- B
Using the most complex deep learning architecture available
Why wrong: Complexity is not an ethical concern.
- C
Mitigating bias in training data that could lead to unfair responses
Bias can lead to unethical treatment of certain groups.
- D
Ensuring the chatbot explains when a customer is speaking to AI
Transparency requires disclosing AI interaction.
- E
Maximizing the number of training epochs
Why wrong: Training epochs are a technical detail, not ethical.
Question 354 of 1,000
AI Associate AI Fundamentals Practice Question
This AI Associate practice question tests your understanding of ai fundamentals. 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 company is deploying an AI chatbot for customer service. Which THREE ethical considerations should be addressed? (Select 3)
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
✓
Protecting customer data privacy during model training and inference
Transparency, bias mitigation, and data privacy are key ethical areas for AI systems.
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.
- ✓
Protecting customer data privacy during model training and inference
Why this is correct
Data privacy is a core ethical and legal requirement.
Related concept
OSPF neighbours must agree on key parameters.
- ✗
Using the most complex deep learning architecture available
Why it's wrong here
Complexity is not an ethical concern.
- ✓
Mitigating bias in training data that could lead to unfair responses
Why this is correct
Bias can lead to unethical treatment of certain groups.
Related concept
OSPF neighbours must agree on key parameters.
- ✓
Ensuring the chatbot explains when a customer is speaking to AI
Why this is correct
Transparency requires disclosing AI interaction.
Related concept
OSPF neighbours must agree on key parameters.
- ✗
Maximizing the number of training epochs
Why it's wrong here
Training epochs are a technical detail, not ethical.
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 practitioner preparing for the AI Associate exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. OSPF neighbour adjacency depends on matching area, hello/dead timers, network type, and authentication — IP reachability alone is not enough. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
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?
AI Fundamentals — This question tests AI Fundamentals — OSPF neighbours must agree on key parameters..
What is the correct answer to this question?
The correct answer is: Protecting customer data privacy during model training and inference — Transparency, bias mitigation, and data privacy are key ethical areas for AI systems.
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: Jul 4, 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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