Describe Artificial Intelligence workloads and considerations →mediumMultiple ChoiceObjective-mapped
AI-900 Practice Question: Describe Artificial Intelligence workloads and considerations
A bank uses an AI system to approve loan applications. The bank wants to ensure that applicants can understand why a loan was approved or rejected. Which Microsoft responsible AI principle is most directly relevant to this requirement?
⚠ Common exam trap
Watch out — candidates often confuse Transparency with Fairness, mistakenly thinking that explaining a decision inherently ensures it is fair, whereas Transparency is solely about providing understandable reasoning, not about the decision's ethical correctness.
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
✓
Transparency
The requirement that applicants can understand why a loan was approved or rejected directly aligns with the Transparency principle, which mandates that AI systems be interpretable and that decisions be explainable to users. In this context, the bank must provide clear reasoning for loan outcomes, enabling applicants to comprehend the factors influencing the decision, such as credit score thresholds or income verification rules.
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 it's wrong here
Fairness is a responsible AI principle concerned with detecting and mitigating harmful bias so that the AI system treats people equitably across protected attributes such as race, gender, or age. While critical for loan approvals, fairness alone does not require the system to explain its individual decisions; a model could be statistically fair yet still be a black box that gives no reasons for a specific denial. The bank's stated need is for applicants to understand their personal loan outcomes, which fairness does not directly address.
- ✗
Inclusiveness
Why it's wrong here
Inclusiveness focuses on designing AI systems that are accessible, usable, and empowering for people of all abilities and backgrounds, often involving user-centered design and accessibility testing. This principle ensures the bank's loan portal and AI interface work well for diverse applicants, but it does not mandate that the underlying model disclose why a particular application was approved or rejected. While inclusive systems may communicate clearly, inclusiveness as a principle is about reach and usability, not interpretability of individual decisions.
- ✓
Transparency
Why this is correct
Transparency is the responsible AI principle that requires AI systems to be open about their use, capabilities, and limitations, and—critically—to provide interpretable or explainable reasons for their decisions. For loan approvals, transparency directly applies because applicants must understand the factors that led to their approval or denial, such as credit history, income, or debt-to-income ratio. This includes using interpretable models, generating post-hoc explanations like SHAP values, or otherwise enabling the bank to give concrete justifications to affected customers, making it the correct principle here.
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Reliability and Safety
Why it's wrong here
Reliability and Safety ensure that the AI system performs consistently, remains robust against errors, and avoids harmful failures—for example, not crashing, not producing wildly wrong credit scores, and maintaining data integrity. These qualities are essential for any bank system, but they are about operational correctness and security, not about explaining decision outcomes. A loan model can be highly reliable and accurate while still being completely opaque, so this principle does not answer the requirement for applicants to understand their individual loan decisions.
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Responsible AI Principles
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.
Key term
Transparency
Transparency in AI means that the inner workings, decision-making processes, and data used by an AI system are open, understandable, and auditable by humans.
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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.