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 concerns machine learning bias that yields systematically worse outcomes or resource allocation for groups defined by protected attributes such as race, gender, or age. A vehicle failing in snow does so regardless of the identity of its occupants or pedestrians—it is a distribution-shift failure caused by degraded sensor inputs, not an unequal treatment engendered by biased training data. Therefore, fairness audits and disparate-impact metrics would not identify this defect, because the failure mode is environmental rather than demographic.

  When this WOULD be correct

  A loan approval AI denies applications from a specific ethnic group at a higher rate than others because the training data overrepresents that group in defaults. This violates the Fairness principle.

• ✓

  Reliability and Safety

  Why this is correct

  Reliability and Safety is the governing principle: autonomous navigation is a high-stakes, physically embodied AI where a known failure under snowy conditions means the system has not been validated for a realistic operational envelope. Microsoft's RAI framework requires rigorous testing, clear performance constraints, and graceful degradation before deployment, so shipping a vehicle that predictably fails in wintry weather violates the core safety mandate and creates imminent collision risk.

• ✗

  Privacy and Security

  Why it's wrong here

  Privacy and Security address confidentiality of personal data, resistance to adversarial manipulation, and system integrity against unauthorized access. A navigation system that is unreliable in snow is a functional safety defect, not a data breach or a security vulnerability; no personal information is exposed and no external attacker is involved. While adversarial inputs could in principle cause misperception, the scenario describes ordinary weather, so the root cause resides in robustness testing and safety engineering, not in threat models or privacy controls.

  When this WOULD be correct

  A healthcare AI system that stores patient medical records in an unencrypted cloud database, leading to a data breach. The question would ask which responsible AI principle is violated, and the correct answer would be Privacy and Security.

• ✗

  Inclusiveness

  Why it's wrong here

  Inclusiveness is about removing barriers so that diverse human users—across abilities, languages, and contexts—can access and benefit from AI. Snowy-condition navigation failure does not exclude any person or demographic from using the system; it is a physical-robustness defect in environmental perception and control, not an accessibility gap. The principle would come into play if, for example, the interface were unusable by non-native speakers or the vehicle could not accommodate wheelchair users.

  When this WOULD be correct

  If the question described an AI system that performs poorly for certain demographic groups (e.g., people with disabilities or non-native speakers) due to lack of diverse data, then the violated principle would be inclusiveness.

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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