AI-900 Practice Question: Describe Artificial Intelligence workloads and considerations

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 in AI focuses on eliminating bias and ensuring equitable treatment across demographic groups, such as race, gender, or age, in the screening process. While fairness is critical for job applications, it concerns the model's outcomes and decision parity, not the usability of the interface for individuals with disabilities. Accessibility is a matter of universal design and inclusive interaction, which is a separate principle from algorithmic fairness in Microsoft's responsible AI framework.

  When this WOULD be correct

  If the question asked about ensuring the AI system does not discriminate against candidates based on gender or ethnicity, then Fairness would be the correct principle to prioritize.

• ✗

  Reliability and safety

  Why it's wrong here

  Reliability and safety ensure that the AI system performs consistently, avoids failures, and operates within defined safety parameters under expected conditions. While a job-screening system must be robust against data errors or model drift, this principle does not address the usability of the interface for people with sensory or physical impairments. Accessibility is a distinct concern about human-computer interaction, not about system correctness or failure modes, which is why this option does not match the stated scenario.

  When this WOULD be correct

  This option would be correct if the question asked about ensuring the AI system performs consistently without errors or harmful outcomes, such as in a medical diagnosis system where incorrect predictions could endanger patients.

• ✓

  Inclusiveness

  Why this is correct

  Inclusiveness in Microsoft's responsible AI principles mandates that AI systems are designed to empower everyone, including people with disabilities, by removing barriers and accommodating diverse needs. For an AI screening system, this means the interface must support assistive technologies, provide alternative text, ensure keyboard navigability, and respect visual, auditory, motor, and cognitive impairments. This principle directly targets accessibility gaps, making it the correct choice for a scenario focused on applicants with impairments.

• ✗

  Transparency

  Why it's wrong here

  Transparency refers to an AI system's ability to provide clear, interpretable explanations of its decisions, such as why a particular applicant was shortlisted or rejected. This principle helps build trust and enables human oversight, but it does not inherently require the interface to be accessible to users with disabilities. The scenario's focus is on allowing all users to effectively operate the system, not on explaining its internal logic, making Transparency an inadequate answer.

  When this WOULD be correct

  Transparency would be correct if the question asked about ensuring candidates understand how the AI screening system makes decisions, or if the system must provide explanations for its recommendations.

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.

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?”

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