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

An autonomous delivery robot uses AI to navigate sidewalks. The robot occasionally fails to detect pedestrians in low-light conditions, leading to near-collisions. The company wants to ensure the system is robust and safe before wider deployment. Which Microsoft responsible AI principle is most directly relevant?

⚠ Common exam trap

A common mix-up: candidates confuse Transparency (which involves disclosing limitations) with the actual requirement to engineer the system to be safe and reliable, but the question asks for the principle most directly relevant to preventing near-collisions, which is Reliability and safety.

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

Reliability and safety

The robot's failure to detect pedestrians in low-light conditions directly impacts the system's ability to operate reliably and safely. The Reliability and safety principle in Microsoft's responsible AI framework requires that AI systems perform consistently under expected conditions and fail gracefully when they cannot. Ensuring the robot can handle edge cases like low light is a core safety requirement before deployment.

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 evaluates whether a system produces biased outcomes across protected groups (such as race, gender, or age) in decisions like hiring, lending, or content ranking. Here, the robot's low-light detection failure poses an equal physical hazard to any pedestrian in its path, not a differential or discriminatory treatment of a specific subgroup. Since the risk is collision and injury rather than inconsistent service or allocation, fairness is not the governing principle for this safety-critical perception defect.

  • Privacy and security

    Why it's wrong here

    Privacy and security pertain to protecting personal data from unauthorized access, misuse, or disclosure, and to hardening systems against cyberattacks. The scenario describes a perception failure in low light that causes the robot to miss pedestrians, which is a matter of sensor reliability and collision avoidance, not a data breach or a compromise of information confidentiality. Even if the robot collects location or visual data, the immediate harm is physical, not informational, so this principle is misapplied to the stated problem.

  • Reliability and safety

    Why this is correct

    Reliability and safety are central to autonomous systems because they require the AI to perform correctly under all expected conditions, including degraded lighting, and to fail safely without causing harm. A false negative in pedestrian detection directly violates the core safety guarantee of sidewalk navigation, as it can lead to a collision with a human. This principle mandates robust perception, validation across environmental edge cases, and fallback behaviors when confidence is low, making it the correct lens for this low-light failure.

  • Transparency

    Why it's wrong here

    Transparency focuses on making an AI system's decisions interpretable or auditable, often through explanations, logs, or model interpretability tools, so that operators and users can understand why a particular output occurred. While a post-incident log of misdetections might help diagnose the low-light weakness, the immediate need is to prevent the robot from hitting pedestrians, not to explain its failures to bystanders or auditors at that moment. The robot's operation does not inherently require real-time decision explanations; the primary deficiency is unsafe perception, not a lack of insight.

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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.