- A
Fairness
Correct. Fairness is about ensuring AI systems do not discriminate against individuals or groups. The disparate false positive rate across ethnic groups is a fairness issue.
- B
Reliability and safety
Why wrong: Incorrect. Reliability and safety focus on the system performing dependably and without causing harm. While a high false positive rate could cause harm, the core issue here is disparate impact across groups, which falls under fairness.
- C
Privacy and security
Why wrong: Incorrect. Privacy and security concern data protection and unauthorized access; the scenario does not describe a data breach or privacy violation.
- D
Accountability
Why wrong: Incorrect. Accountability means that people are responsible for AI systems and their outcomes, but the immediate concern is the unfair treatment of a group, not who is responsible.
AI-900 Practice Question: Describe Artificial Intelligence workloads and considerations
This AI-900 practice question tests your understanding of describe artificial intelligence workloads and considerations. 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 hospital deploys an AI system that predicts patient readmission risk within 30 days of discharge. The model uses features such as age, medical history, and treatment plans. The hospital discovers that the model has a significantly higher false positive rate for patients of a certain ethnic group compared to others, even though the model's overall accuracy is similar across groups. This disparity was not intentional. Which Microsoft responsible AI principle is most directly compromised?
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
Fairness
The Fairness principle requires AI systems to treat all groups equitably and avoid discrimination. A higher false positive rate for one ethnic group, even if unintentional, represents an unfair disparity. While Inclusiveness relates to designing for all people, Fairness specifically addresses equitable outcomes and bias mitigation, so it is the most directly compromised principle in this case.
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.
- ✓
Fairness
Why this is correct
Correct. Fairness is about ensuring AI systems do not discriminate against individuals or groups. The disparate false positive rate across ethnic groups is a fairness issue.
Related concept
Static NAT maps one inside address to one outside address.
- ✗
Reliability and safety
Why it's wrong here
Incorrect. Reliability and safety focus on the system performing dependably and without causing harm. While a high false positive rate could cause harm, the core issue here is disparate impact across groups, which falls under fairness.
- ✗
Privacy and security
Why it's wrong here
Incorrect. Privacy and security concern data protection and unauthorized access; the scenario does not describe a data breach or privacy violation.
- ✗
Accountability
Why it's wrong here
Incorrect. Accountability means that people are responsible for AI systems and their outcomes, but the immediate concern is the unfair treatment of a group, not who is responsible.
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.
Trap categories for this question
Scenario analysis trap
Incorrect. Privacy and security concern data protection and unauthorized access; the scenario does not describe a data breach or privacy violation.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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-900 NAT questions on configuration and troubleshooting.
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FAQ
Questions learners often ask
What does this AI-900 question test?
Describe Artificial Intelligence workloads and considerations — This question tests Describe Artificial Intelligence workloads and considerations — Static NAT maps one inside address to one outside address..
What is the correct answer to this question?
The correct answer is: Fairness — The Fairness principle requires AI systems to treat all groups equitably and avoid discrimination. A higher false positive rate for one ethnic group, even if unintentional, represents an unfair disparity. While Inclusiveness relates to designing for all people, Fairness specifically addresses equitable outcomes and bias mitigation, so it is the most directly compromised principle in this case.
What should I do if I get this AI-900 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-900 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: May 17, 2026
This AI-900 practice question is part of Courseiva's free Microsoft 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-900 exam.
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