AI-900 Practice Question: Describe features of computer vision workloads on Azure
What is facial recognition and what are the key responsible AI considerations for its use?
⚠ Common exam trap
Candidates often assume facial recognition is either harmless or perfectly accurate, ignoring the documented bias and privacy risks that responsible AI frameworks like Microsoft's Responsible AI Standard explicitly address.
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
✓
Facial recognition requires ethical consideration regarding accuracy disparities, privacy, and potential for misuse
Facial recognition is a computer vision technology that identifies or verifies individuals by analyzing facial features from images or video. The key responsible AI considerations include addressing accuracy disparities across demographic groups (e.g., higher false positive rates for certain ethnicities), ensuring privacy through data minimization and consent, and preventing misuse such as mass surveillance without oversight. Option B correctly captures these ethical imperatives, which are critical for trustworthy 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.
- ✗
Facial recognition has no ethical concerns and should be deployed universally
Why it's wrong here
This claim ignores documented ethical and societal risks in facial recognition. Research and incident reports show biased outcomes across skin tones and genders, privacy violations from mass surveillance, and the potential for abuse by authoritarian states or malicious actors. Deploying it universally without safeguards would amplify these harms rather than mitigate them, contradicting the shared responsibility all AI practitioners carry.
- ✓
Facial recognition requires ethical consideration regarding accuracy disparities, privacy, and potential for misuse
Why this is correct
This is the correct stance because facial recognition systems carry real trade-offs that demand governance. Accuracy disparities among demographic groups violate the fairness principle, collecting or storing biometric data raises privacy and consent issues, and the technology can be misused for surveillance or fraud. Responsible deployment requires impact assessments, human oversight, transparency, and data-protection measures to align with Microsoft's responsible AI principles.
- ✗
Facial recognition is only used for unlocking smartphones
Why it's wrong here
Facial recognition is a general-purpose Azure AI capability used for identity verification, access control, surveillance, photo tagging, border security, fraud detection, and many other systems. Restricting its use to smartphone unlock underestimates its pervasiveness and the ethical consequences that scale with deployment. Even when embedded in consumer devices, the same risks of bias, privacy, and misuse apply at smaller scale, so the responsible AI considerations are never absent.
- ✗
Facial recognition is 100% accurate across all demographics
Why it's wrong here
No facial recognition algorithm is perfectly accurate for every person; accuracy varies measurably with age, skin tone, lighting, pose, and other factors. Independent evaluations like NIST FRVT have shown higher false-match and false-nonmatch rates for certain demographic groups, particularly across darker skin tones and female subjects. Claiming 100% accuracy is therefore empirically false and dangerous, because it would encourage unchecked reliance on a system with known performance gaps.
Go deeper
Related to this question
Learn chapter
Responsible AI Principles
Key term
Computer vision
Computer vision is a field of artificial intelligence that enables computers to interpret and make decisions based on visual data from the world, such as images and videos.
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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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.