AI-900 Practice Question: Describe Artificial Intelligence workloads and considerations
A manufacturing company deploys an AI system to predict equipment failures from sensor data. They need to ensure the system continues to function correctly even if some sensors malfunction or provide noisy data. Which responsible AI principle is most directly relevant?
⚠ Common exam trap
Many exam-takers confuse 'reliability and safety' with 'transparency' because they think explaining why a sensor failed is the same as ensuring the system works despite the failure, but transparency is about post-hoc explanation, not operational robustness.
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
✓
B: Reliability and safety
The scenario describes an AI system that must continue to function correctly despite sensor malfunctions or noisy data. This directly aligns with the reliability and safety principle, which ensures that AI systems operate consistently and safely under varying conditions, including edge cases and unexpected inputs. In predictive maintenance, robustness to sensor noise is critical to avoid false alarms or missed failures.
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 is concerned with preventing algorithmic bias and ensuring equitable treatment across demographic or social groups, such as race, gender, or age. It does not address how an AI system responds when input data is corrupted or missing, like a failed sensor. Sensor faults are a data integrity and system robustness issue, not an equity or bias issue, so Fairness is the wrong principle here.
- ✓
B: Reliability and safety
Why this is correct
Reliability and safety is the correct principle because it directly governs the expectation that an AI system will function consistently and without causing harm, even when inputs are unexpected or faulty. In a manufacturing context, a defective sensor can produce out-of-distribution values, and a reliable system must detect this anomaly, degrade gracefully, or trigger a safe fallback rather than making confident but erroneous predictions. This principle mandates rigorous testing, monitoring, and fail-safe design to preserve operational safety and avoid costly or dangerous failures.
- ✗
C: Transparency
Why it's wrong here
Transparency is about making the AI system's behavior understandable to humans, such as providing explanations for predictions, disclosing model limitations, and ensuring interpretability of decisions. It does not involve detecting or compensating for faulty sensor inputs; that is an engineering concern about input validation and system robustness. While transparency would help users trust the system, it would not prevent a model from making bad predictions when fed corrupted sensor data, so it is not the relevant principle here.
- ✗
D: Accountability
Why it's wrong here
Accountability refers to the assignment of clear roles, responsibilities, and governance around an AI system, including who owns decisions, how audits are conducted, and ensuring human oversight. It is a managerial and legal framework rather than a technical property that enables the system to tolerate sensor failures. Accountability would ensure someone is blamed or responsible after a failure, but it does not make the system itself robust to faulty inputs, so it does not address the prompt's concern.
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Related to this question
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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
Reliability and safety
Reliability and safety in IT means that systems consistently perform their intended functions without failure and that they operate without causing harm to people, data, or the environment.
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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.