Describe Artificial Intelligence workloads and considerations →mediumMultiple ChoiceObjective-mapped
AI-900 Practice Question: Describe Artificial Intelligence workloads and considerations
A financial institution uses an AI model to assess creditworthiness for loan applications. After deployment, they discover that the model assigns higher risk scores to applicants from certain postal codes, which are predominantly low-income minority neighborhoods. The model's predictions are accurate according to historical data, but the bank is concerned about ethical implications. Which Microsoft responsible AI principle is most directly applicable to addressing this issue?
⚠ Common exam trap
Candidates often confuse 'accuracy according to historical data' with ethical validity, leading them to overlook Fairness and instead choose Reliability and Safety, thinking the model is 'correct' and thus reliable.
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 model's assignment of higher risk scores based on postal codes, which correlate with low-income minority neighborhoods, directly violates the Fairness principle. This principle requires AI systems to treat all groups equitably and avoid reinforcing societal biases, even if the model's predictions are statistically accurate according to historical data. The bank's ethical concern centers on disparate impact, which fairness assessments (e.g., demographic parity or equal opportunity metrics) are designed to detect and mitigate.
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
Fairness addresses the potential for AI systems to create or reinforce unfair biases, such as differential treatment based on location or demographics. In this scenario, the model's decisions vary inappropriately by postal code or income, which are protected attributes or proxy variables in lending. A fair model should produce equitable outcomes across groups, ensuring that creditworthiness is evaluated using relevant factors only. This principle directly targets the bias described, making it the correct answer.
- ✗
Inclusiveness
Why it's wrong here
Inclusiveness is a broad principle that focuses on empowering all people, including those with disabilities, by ensuring AI systems are accessible and usable by diverse populations. It does not specifically address the issue of unfair bias based on postal code or income; rather, it emphasizes user-centered design and universal access. The problem here is about disparate impact in loan decisions, not about accessibility features or inclusion of marginalized user groups. Therefore, inclusiveness is not the appropriate principle for this situation.
- ✗
Reliability and Safety
Why it's wrong here
Reliability and Safety ensure that an AI system performs consistently, behaves predictably under various conditions, and avoids causing harm through failures or erroneous outputs. In this case, the model is stated to be accurate for all groups, so the concern is not a lack of reliability, system crashes, or unsafe decisions. The model may be technically reliable but still exhibit bias, because a system can be internally consistent and accurate overall while systematically disadvantaging certain groups. Since the issue is about unfair outcomes, not system malfunctions, this principle does not apply.
- ✗
Privacy and Security
Why it's wrong here
Privacy and Security involve protecting sensitive data from unauthorized access, breaches, and misuse, and ensuring compliance with regulations like GDPR. The scenario describes a bias in lending decisions, not a data leak or security vulnerability; the data could be perfectly secure while the model still produces discriminatory outcomes. Fairness issues are independent of data protection, because bias arises from historical patterns and algorithmic design, not from insecure handling of information. Therefore, this principle does not address the fundamental problem presented.
Quick reference
RAID Level Comparison
| RAID Level | Min Disks | Fault Tolerance | Read | Write | Usable Capacity |
|---|---|---|---|---|---|
| RAID 0 | 2 | None | Excellent | Excellent | 100% |
| RAID 1 | 2 | 1 disk | Good | Moderate | 50% |
| RAID 5 | 3 | 1 disk | Good | Moderate | 67–94% |
| RAID 6 | 4 | 2 disks | Good | Lower | 50–88% |
| RAID 10 | 4 | 1 disk per mirror | Excellent | Good | 50% |
RAID is not a backup strategy — it protects against disk failure but not against accidental deletion, ransomware, or site-level events.
Go deeper
Related to this question
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Responsible AI Principles
Key term
Model
In IT and AI, a model is a trained mathematical representation that learns patterns from data to make predictions or decisions.
Key term
Responsible AI
A framework of ethical principles and practices that ensure artificial intelligence systems are developed and deployed in a transparent, fair, accountable, and safe manner.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.