The answer is to conduct a fairness audit and involve medical experts to review the model's decisions. This is correct because bias detection first steps require a dual approach: a technical fairness audit systematically measures disparities in model outputs, while domain experts—in this case, medical professionals—validate whether the prioritization logic aligns with clinical ethics and patient needs. On the Salesforce AI Associate exam, this scenario tests your understanding that ethical AI remediation is a collaborative process, not a purely technical fix; a common trap is choosing to immediately disable the system, which ignores the need for measured, evidence-based action. Remember the memory tip: "Audit then Advise"—always combine data-driven analysis with human expertise before making changes.
AI Associate Ethical Considerations of AI Practice Question
This AI Associate practice question tests your understanding of ethical considerations of 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 healthcare organization uses AI to prioritize patient appointments. The AI gives lower priority to patients with a specific chronic condition. To ensure ethical AI, what should the organization do?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Conduct a fairness audit and involve medical experts to review the model's decisions
Option B (Conduct a fairness audit and involve medical experts to review the model's decisions) is correct because it combines technical assessment with domain expertise. Option A (immediately disable the AI) may be too drastic. Option C (accept the model as is) ignores the bias. Option D (train the model on more data of that condition) might not correct the prioritization logic.
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.
✗
Train the model on more data from patients with that condition
Why it's wrong here
More data may not fix the underlying bias in prioritization rules.
✗
Immediately disable the AI system and revert to manual scheduling
Why it's wrong here
Disabling may not be necessary if bias can be corrected.
✗
Accept the model's decisions since they are based on data
Why it's wrong here
Data-driven decisions can still be unfair.
✓
Conduct a fairness audit and involve medical experts to review the model's decisions
Why this is correct
Combining technical and domain expertise ensures ethical oversight.
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.
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: Conduct a fairness audit and involve medical experts to review the model's decisions — Option B (Conduct a fairness audit and involve medical experts to review the model's decisions) is correct because it combines technical assessment with domain expertise. Option A (immediately disable the AI) may be too drastic. Option C (accept the model as is) ignores the bias. Option D (train the model on more data of that condition) might not correct the prioritization logic.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A company uses an AI model to screen job applications. They discover the model is less likely to recommend female candidates. What should the company prioritize first?
easy
A.Remove gender-related features from the model.
B.Re-train the model with only male candidates.
✓ C.Implement a fairness metric to evaluate bias.
D.Increase the model's training data size.
Why C: Option D is correct because the first step is to measure the extent of bias using fairness metrics before taking corrective action. Option A is wrong because training only on males would worsen bias. Option B is wrong because removing features may not eliminate proxy bias. Option C is wrong because simply increasing data may not address the underlying bias.
Variation 2. A company deploys an AI system to screen job applications. The system is found to consistently reject candidates from a particular university, even though those candidates are qualified. What is the most ethical first step?
medium
A.Increase rejections from other universities to balance
B.Ignore the finding as correlation, not causation
C.Change the screening criteria to include more universities
✓ D.Investigate the training data and model for bias
Why D: The correct answer is A because investigating the data and model for bias is the appropriate ethical action. Option B is wrong because ignoring the bias violates fairness. Option C is wrong because immediately increasing rejections is unethical. Option D is wrong because changing the screening criteria without analysis may not address the root cause.
Last reviewed: Jun 23, 2026
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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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