Describe Artificial Intelligence workloads and considerations →mediumMultiple ChoiceObjective-mapped
AI-900 Practice Question: Describe Artificial Intelligence workloads and considerations
An autonomous drone delivery company uses an AI model to navigate. During testing in a new city, the model fails to detect power lines and crashes into them. The company wants to ensure their system is robust to unusual conditions. Which Microsoft responsible AI principle is most directly relevant?
⚠ Common exam trap
It's easy for candidates to confuse 'Reliability and Safety' with 'Privacy and Security' because both involve 'security' in a broad sense, but the question specifically targets physical safety and system robustness, not data protection.
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 scenario describes a failure in an AI system that leads to a physical safety hazard (crashing into power lines). The Microsoft responsible AI principle of Reliability and Safety directly addresses the need for AI systems to operate reliably under a range of conditions and to fail safely when they encounter unexpected situations. Ensuring robustness to unusual conditions, such as unseen power lines in a new city, is a core requirement of this principle.
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 is concerned with eliminating algorithmic bias so that the model treats all demographic groups and neighborhoods equitably—for example, ensuring delivery coverage, pricing, or approval rates do not systematically disadvantage certain communities. While an autonomous drone's fairness metrics might reveal disparities in service accuracy, those metrics say nothing about whether the drone's perception system can safely avoid a utility pole in fog, handle actuator failure, or land without injuring bystanders. Thus, fairness is a social-equity lens, not an engineering assurance for physical security.
- ✗
Privacy and Security
Why it's wrong here
Privacy and Security specifically protects the confidentiality, integrity, and availability of data and system interfaces—through encryption, access controls, anti-spoofing, and resistance to adversarial cyberattacks. A drone delivery model could be perfectly secure against hackers yet still crash into trees because of weak object detection or poor fail-safe logic, because cyber hygiene does not validate the model's physical behavior under normal or degraded flight conditions. For this question, Privacy and Security is the wrong answer because it addresses unauthorized access and data leakage, not safe and dependable autonomy.
- ✓
Reliability and Safety
Why this is correct
Reliability and Safety is the correct principle because it mandates that an AI system perform consistently under expected conditions and degrade gracefully under unexpected ones—including sensor malfunctions, GPS outages, wind gusts, or unseen obstacles. For autonomous drones, this translates into rigorous validation against edge cases, redundant navigation paths, conservative decision thresholds, and fail-safe actions like emergency landing. This principle directly mitigates the risk of physical harm to people and property, which is exactly what an AI model for drone delivery must guarantee.
- ✗
Inclusiveness
Why it's wrong here
Inclusiveness centers on making AI accessible and useful to the widest possible range of people, including those with disabilities, different languages, or non-standard needs—such as offering a delivery app with screen-reader compatibility or support for low-bandwidth regions. While an inclusive design might broaden who can use the drone service, it does not influence the onboard autonomy's ability to judge a safe landing zone or maintain stable flight in high winds. Therefore, inclusiveness is not the principle that addresses the drone's physical robustness and safety.
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Responsible AI Principles
Key term
Model
In IT and AI, a model is a trained mathematical representation that learns patterns from data to make predictions or decisions.
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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JA
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.