AI-900 Practice Question: Describe Artificial Intelligence workloads and considerations
A company develops an AI system to predict employee performance based on work habits. The system uses complex neural networks and its decisions are not easily interpretable. The company wants to ensure that employees can understand why a particular performance prediction was made. Which Microsoft responsible AI principle is most directly relevant?
⚠ Common exam trap
Many candidates confuse 'transparency' with 'fairness' because both involve ethical AI, but transparency specifically addresses the 'why' behind a decision, not the absence of bias.
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
✓
C) Transparency
Transparency is the responsible AI principle that directly addresses the need for interpretability and explainability of AI systems. In this scenario, the company uses complex neural networks that are inherently black-box models, making their decisions difficult to understand. Transparency requires that the system provides explanations for its predictions, enabling employees to comprehend why a particular performance rating was assigned, which aligns with the goal of building trust and accountability.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
A) Fairness
Why it's wrong here
Fairness in AI evaluates whether predictions are free from demographic bias, typically measured across protected groups (e.g., gender, ethnicity) using metrics like statistical parity or equalized odds. While an employee who receives an unexpected prediction might suspect bias, fairness alone does not require the system to reveal the input features or decision rules that drove that specific outcome. Thus, fairness addresses equitable treatment, not the interpretive reasoning behind an individual prediction, making it an incomplete match to the stated goal.
- ✗
B) Reliability and safety
Why it's wrong here
Reliability and safety in AI focus on consistent performance under expected conditions and the prevention of harmful failures, often verified through robustness testing, fallback mechanisms, and monitoring for drift. These engineering properties ensure the model behaves predictably and avoids catastrophic errors, but they do not stipulate that the system's internal logic be communicated in human-understandable terms. Therefore, while a reliable system may produce consistent outputs, it could still be a black box, failing to provide the causal explanations employees need.
- ✓
C) Transparency
Why this is correct
Transparency is the AI principle that demands systems operate in an interpretable manner, with decisions that can be traced back to specific inputs and logic, often implemented through explainability techniques like feature attribution or rule extraction. By enabling employees to see exactly why a prediction was made—for example, which performance indicators most influenced the outcome—transparency directly fulfills the company's requirement for understanding. It goes beyond merely stating a result, obligating the model to offer clear, actionable reasons that stakeholders can inspect and challenge.
- ✗
D) Privacy and security
Why it's wrong here
Privacy and security in AI concern the protection of data at rest, in transit, and during model training, using measures like encryption, access controls, and differential privacy to prevent unauthorized disclosure or misuse. These safeguards are orthogonal to explanation needs; a system can be fully secure and private while still producing predictions that are opaque to end users. Since the question centers on comprehensible prediction outcomes, privacy and security address confidentiality, not interpretability, so they do not meet the identified goal.
Go deeper
Related to this question
Learn chapter
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
Accountability
Accountability is the security principle that ensures actions and identity are linked so that a person or system can be held responsible for their activities.
About these practice questions
One of 985 original AI-900 practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
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.