AI-900 Practice Question: Describe Artificial Intelligence workloads and considerations
A bank deploys an AI system to approve personal loans. The system uses a complex deep learning model that produces a decision (approve or reject) without any explanation of why. Loan applicants who are rejected are not given any reason. According to Microsoft's responsible AI principles, which principle is most directly violated by this system?
⚠ Common exam trap
Many candidates confuse the lack of explanation with fairness or privacy issues, but the core violation is the absence of transparency, which is explicitly about providing understandable reasoning for AI decisions.
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 system's inability to provide any explanation for its loan approval or rejection decisions directly violates the transparency principle. Microsoft's responsible AI principle of transparency requires that AI systems be understandable and that users be informed about how decisions are made, including the factors that influenced the outcome. A black-box deep learning model that gives no reasoning or feedback to rejected applicants fails this requirement.
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 relates to eliminating bias and ensuring equitable treatment across different demographic groups. While the lack of explanation could make it difficult to audit for bias, the scenario itself does not provide any evidence of discrimination or disparate impact on protected groups. The principal described problem is the absence of reasoning, not an indication of unfair outcomes; therefore, fairness is not the direct violation here.
When this WOULD be correct
This option would be correct if the question described an AI system that consistently rejects loan applications from a specific demographic group (e.g., based on race or gender) without legitimate justification, directly violating the fairness principle.
- ✓
Transparency
Why this is correct
The core principle of transparency in responsible AI is that systems should be explainable and understandable to users. In this scenario, the AI system approves loans without providing any rationale, meaning applicants cannot understand why a credit decision was made. This lack of explainability is a direct violation of transparency, which is a fundamental expectation in financial services under regulations like the GDPR's right to explanation and ECOA's adverse action notice requirements.
- ✗
Reliability and safety
Why it's wrong here
This principle focuses on the system performing consistently and correctly, avoiding harm from errors or unexpected failures. The scenario describes a system that works as intended but simply doesn't offer explanations—there's no mention of inaccurate predictions, system crashes, or unsafe outputs. As such, the core issue is not about whether the system functions reliably, but about its lack of interpretability, so this principle is not the primary concern.
When this WOULD be correct
This option would be correct if the question described an AI system that frequently makes incorrect loan approvals or rejections due to model errors, or fails to handle edge cases safely, leading to financial harm.
- ✗
Privacy and security
Why it's wrong here
This principle involves protecting personal data from unauthorized access, misuse, or loss, and ensuring compliance with data protection laws. The loan scenario involves sensitive financial data, but the problem statement does not include any data breach, insecure storage, or unauthorized sharing. Without such issues, the system's failure is again about explainability rather than data protection, making privacy and security an incorrect option.
When this WOULD be correct
This option would be correct if the question described a scenario where the AI system exposes sensitive applicant data without consent, or fails to protect loan application information from unauthorized access, violating privacy and security principles.
Option-by-option analysis
Why each answer is right or wrong
Understanding why wrong answers are wrong — and when they would be correct — is what separates a 750 score from a 900. The AI-900 exam frequently reuses these exact scenarios with slightly different constraints.
✓TransparencyCorrect answer▾
Why this is correct
The core principle of transparency in responsible AI is that systems should be explainable and understandable to users. In this scenario, the AI system approves loans without providing any rationale, meaning applicants cannot understand why a credit decision was made. This lack of explainability is a direct violation of transparency, which is a fundamental expectation in financial services under regulations like the GDPR's right to explanation and ECOA's adverse action notice requirements.
✗FairnessWrong answer — click to see why▾
Why this is wrong here
The system's lack of explanation for loan decisions violates transparency, not fairness. Fairness would be violated if the model exhibited bias against protected groups, but the question focuses on the absence of reasoning, not on discriminatory outcomes.
★ When this WOULD be the correct answer
This option would be correct if the question described an AI system that consistently rejects loan applications from a specific demographic group (e.g., based on race or gender) without legitimate justification, directly violating the fairness principle.
Why candidates choose this
Candidates may associate loan approval systems with fairness concerns (e.g., bias against minorities) and overlook that the core issue here is the lack of explanation, which falls under transparency.
✗Reliability and safetyWrong answer — click to see why▾
Why this is wrong here
The system's lack of explanation for loan decisions directly violates transparency, not reliability and safety. Reliability and safety concern system accuracy and robustness, which are not questioned here.
★ When this WOULD be the correct answer
This option would be correct if the question described an AI system that frequently makes incorrect loan approvals or rejections due to model errors, or fails to handle edge cases safely, leading to financial harm.
Why candidates choose this
Candidates may confuse 'lack of explanation' with 'unreliable' because they assume an opaque system is more likely to be wrong, but the principle violated is transparency, not reliability.
✗Privacy and securityWrong answer — click to see why▾
Why this is wrong here
The question focuses on the lack of explanation for loan decisions, which directly violates transparency. Privacy and security are not the primary issue here, as no data breach or misuse of personal information is described.
★ When this WOULD be the correct answer
This option would be correct if the question described a scenario where the AI system exposes sensitive applicant data without consent, or fails to protect loan application information from unauthorized access, violating privacy and security principles.
Why candidates choose this
Candidates may confuse the lack of explanation with a privacy concern, thinking that not providing reasons is a form of hiding information, which they incorrectly associate with privacy violations.
Analysis generated from the official AI-900blueprint and verified against question context. The “when correct” sections are what AI assistants cite when candidates ask “what’s the difference between these options?”
Go deeper
Related to this question
Learn chapter
Responsible AI Principles
Key term
Deep learning
Deep learning is a subset of machine learning that uses multi-layered neural networks to automatically learn patterns from large amounts of data.
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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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.