- A
Facial recognition has no ethical concerns and should be deployed universally
Why wrong: Facial recognition has significant ethical concerns including bias, privacy, and misuse potential — responsible use requires careful consideration.
- B
Facial recognition requires ethical consideration regarding accuracy disparities, privacy, and potential for misuse
Responsible facial recognition deployment requires addressing demographic accuracy disparities, obtaining consent, protecting privacy, and preventing misuse.
- C
Facial recognition is only used for unlocking smartphones
Why wrong: Facial recognition has many applications beyond phone unlock — the responsible AI concerns apply across all use cases.
- D
Facial recognition is 100% accurate across all demographics
Why wrong: Accuracy varies across demographic groups — disparities in accuracy across skin tones and ages are a documented fairness concern.
Quick Answer
The correct answer is Option B, which identifies accuracy disparities, privacy, and potential for misuse as the key responsible AI considerations for facial recognition. This is correct because facial recognition technology, a computer vision method that identifies individuals by analyzing facial features, has demonstrated significant accuracy disparities across demographic groups—such as higher false positive rates for certain ethnicities—which can lead to unfair outcomes. Additionally, the technology raises serious privacy concerns around data minimization and consent, and it risks being deployed for mass surveillance without proper oversight. On the Microsoft Azure AI Fundamentals AI-900 exam, this topic tests your understanding of responsible AI principles within the context of computer vision workloads, often appearing as a scenario-based question where you must distinguish ethical safeguards from purely technical capabilities. A common trap is focusing on model accuracy alone while ignoring societal impact. Memory tip: think of the three Ps—Performance disparities, Privacy, and Potential for misuse—to recall the core ethical pillars.
AI-900 Practice Question: Describe features of computer vision workloads on Azure
This AI-900 practice question tests your understanding of describe features of computer vision workloads on azure. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
What is facial recognition and what are the key responsible AI considerations for its use?
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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
Facial recognition has significant ethical concerns including bias, privacy, and misuse potential — responsible use requires careful consideration.
- ✓
Facial recognition requires ethical consideration regarding accuracy disparities, privacy, and potential for misuse
Why this is correct
Responsible facial recognition deployment requires addressing demographic accuracy disparities, obtaining consent, protecting privacy, and preventing misuse.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Facial recognition is only used for unlocking smartphones
Why it's wrong here
Facial recognition has many applications beyond phone unlock — the responsible AI concerns apply across all use cases.
- ✗
Facial recognition is 100% accurate across all demographics
Why it's wrong here
Accuracy varies across demographic groups — disparities in accuracy across skin tones and ages are a documented fairness concern.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may 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.
Detailed technical explanation
How to think about this question
Facial recognition systems typically use deep learning models (e.g., convolutional neural networks) to extract facial embeddings—numerical representations of facial features—and compare them against a database. A critical subtlety is that model performance degrades when training data lacks diversity; for example, the Face Recognition Vendor Test (FRVT) by NIST has documented higher false positive rates for African American and Asian faces in some algorithms. Real-world scenarios like airport security or law enforcement must implement fairness metrics (e.g., equalized odds) and transparency reports to mitigate these risks.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
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FAQ
Questions learners often ask
What does this AI-900 question test?
Describe features of computer vision workloads on Azure — This question tests Describe features of computer vision workloads on Azure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this AI-900 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 11, 2026
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.
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