AI-900 Practice Question: Describe Artificial Intelligence workloads and considerations
A company uses an AI system to help screen job applications. The system ranks candidates based on their resumes. The company wants to ensure that if a candidate asks why they were not selected, the company can provide a clear explanation of the factors that influenced the AI's decision. Which Microsoft responsible AI principle is most directly relevant?
⚠ Common exam trap
Microsoft often tests transparency by confusing it with accountability, but the key distinction is that transparency is about explainability of decisions, while accountability is about ownership and governance of the system.
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
Transparency is the responsible AI principle that requires AI systems to be understandable and interpretable. In this scenario, the company needs to provide a clear explanation of why a candidate was not selected, which directly aligns with transparency's goal of making AI decisions explainable to users.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Transparency
Why this is correct
Transparency in AI requires that a system's decisions be interpretable and explainable to the people they affect. For an AI that screens job applications, transparency means job applicants can understand why their application was accepted or rejected, including the key factors and logic behind the decision. This principle is central to building trust and is often legally required in hiring contexts, where opaque automated decisions would be unacceptable.
- ✗
Accountability
Why it's wrong here
Accountability is about the organization taking responsibility for the AI system's outcomes, including establishing governance, clear ownership, and processes to address harm or errors. It does not specifically mandate that each individual decision be explained to the affected person; instead, it focuses on who is answerable for the system's overall behavior. In this scenario, accountability would mean the company owns the consequences of the screening tool, but it wouldn't by itself guarantee that candidates receive explanations for their specific results.
- ✗
Privacy and security
Why it's wrong here
Privacy and security are concerned with protecting sensitive data, such as candidates' personal information, from unauthorized access, misuse, or breaches. These principles ensure that data handling complies with regulations and ethical standards, but they say nothing about explaining how the AI made a particular decision. While important in an AI hiring system, privacy and security do not address the need for interpretability or the reasoning behind an application's outcome.
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Reliability and safety
Why it's wrong here
Reliability and safety focus on the AI system performing consistently, accurately, and without causing harm, such as avoiding errors, biases, or unexpected failures. A reliable and safe system could still be a black box—it might produce correct results without giving any insight into why. Therefore, while these principles are essential for ensuring the screening tool works as intended, they do not inherently require that decisions be explained to affected job applicants.
Go deeper
Related to this question
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Responsible AI Principles
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.
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.