- A
Use a single fairness metric to evaluate the model
Why wrong: Fairness is multidimensional; one metric is inadequate.
- B
Deploy the model quickly to gather real-world data
Why wrong: Rapid deployment without fairness checks can harm marginalized groups.
- C
Remove all sensitive attributes from the data
Why wrong: Sensitive attributes may be proxied by other features; removal alone is insufficient.
- D
Test model performance on different demographic groups
Disaggregated testing reveals performance disparities.
- E
Involve diverse stakeholders in model development
Diverse perspectives help identify potential biases.
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 company wants to ensure their AI is fair. Which TWO steps are appropriate?
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
Test model performance on different demographic groups
Options B and D are correct. Testing the model on different demographic groups helps identify disparities, and involving diverse stakeholders brings multiple perspectives. Option A is wrong because removing all sensitive attributes may not eliminate bias due to proxy features. Option C is wrong because a single metric cannot capture all fairness aspects. Option E is wrong because deploying quickly without testing can exacerbate unfairness.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
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 single fairness metric to evaluate the model
Why it's wrong here
Fairness is multidimensional; one metric is inadequate.
- ✗
Deploy the model quickly to gather real-world data
Why it's wrong here
Rapid deployment without fairness checks can harm marginalized groups.
- ✗
Remove all sensitive attributes from the data
Why it's wrong here
Sensitive attributes may be proxied by other features; removal alone is insufficient.
- ✓
Test model performance on different demographic groups
Why this is correct
Disaggregated testing reveals performance disparities.
Related concept
Static NAT maps one inside address to one outside address.
- ✓
Involve diverse stakeholders in model development
Why this is correct
Diverse perspectives help identify potential biases.
Related concept
Static NAT maps one inside address to one outside address.
Common exam traps
Common exam trap: NAT rules depend on direction and matching traffic
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Detailed technical explanation
How to think about this question
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
KKey Concepts to Remember
- Static NAT maps one inside address to one outside address.
- PAT allows many inside hosts to share one public address using ports.
- Inside local and inside global describe the private and translated addresses.
- NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
- Identify inside and outside interfaces first.
- Check whether the scenario needs static NAT, dynamic NAT or PAT.
- Do not confuse NAT matching ACLs with normal packet-filtering intent.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Real-world example
How this comes up in practice
A small business has 20 workstations on the 192.168.1.0/24 network and one public IP from its ISP. The router uses PAT (NAT overload) so all 20 devices share one public address using different source ports. NAT questions test whether you understand the four address terms and which direction each translation applies.
What to study next
Got this wrong? Here's your next step.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related AI Associate NAT questions on configuration and troubleshooting.
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Ethical Considerations of AI — study guide chapter
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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 — Static NAT maps one inside address to one outside address..
What is the correct answer to this question?
The correct answer is: Test model performance on different demographic groups — Options B and D are correct. Testing the model on different demographic groups helps identify disparities, and involving diverse stakeholders brings multiple perspectives. Option A is wrong because removing all sensitive attributes may not eliminate bias due to proxy features. Option C is wrong because a single metric cannot capture all fairness aspects. Option E is wrong because deploying quickly without testing can exacerbate unfairness.
What should I do if I get this AI Associate question wrong?
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related AI Associate NAT questions on configuration and troubleshooting.
What is the key concept behind this question?
Static NAT maps one inside address to one outside address.
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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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