The answer is Azure AI Face and Azure AI Vision Image Analysis. Azure AI Face is a dedicated service built specifically for detecting, identifying, and analyzing human faces in images, offering capabilities like facial attribute recognition and verification. Azure AI Vision Image Analysis includes face detection as part of its broader object detection feature, allowing it to locate faces and provide bounding box coordinates without the specialized facial analysis of the Face API. On the AI-102 exam, this question tests your ability to distinguish between purpose-built services and general-purpose services that happen to overlap in functionality—a common trap is assuming only the Face API can detect faces, when Image Analysis also qualifies. Remember the memory tip: “Face for features, Vision for location” to recall that Face API provides deep analysis while Vision Image Analysis simply spots faces as objects.
AI-102 Implement computer vision solutions Practice Question
This AI-102 practice question tests your understanding of implement computer vision solutions. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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.
Which TWO Azure AI services provide capabilities to detect and analyze faces in images? (Choose two.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Azure AI Face API
Azure AI Face API is specifically for face detection and analysis. Azure AI Vision Image Analysis can also detect faces as part of its object detection. Option C is wrong because Video Indexer analyzes video, not images. Option D is wrong because Custom Vision is not pre-built for faces. Option E is wrong because Document Intelligence is for document analysis.
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.
✗
Azure AI Custom Vision
Why it's wrong here
Custom Vision can be trained for face detection but is not a pre-built face service.
✓
Azure AI Face API
Why this is correct
Face API is dedicated to face detection, recognition, and analysis.
Related concept
Read the scenario before looking for a memorised answer.
✗
Azure AI Document Intelligence
Why it's wrong here
Document Intelligence is for document OCR and extraction, not face analysis.
✓
Azure AI Vision Image Analysis
Why this is correct
Image Analysis includes face detection as one of its features.
Related concept
Read the scenario before looking for a memorised answer.
✗
Azure Video Indexer
Why it's wrong here
Video Indexer analyzes video, not static images.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→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
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Got this wrong? Here's your next step.
Identify which AI-102 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Implement computer vision solutions — This question tests Implement computer vision solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Azure AI Face API — Azure AI Face API is specifically for face detection and analysis. Azure AI Vision Image Analysis can also detect faces as part of its object detection. Option C is wrong because Video Indexer analyzes video, not images. Option D is wrong because Custom Vision is not pre-built for faces. Option E is wrong because Document Intelligence is for document analysis.
What should I do if I get this AI-102 question wrong?
Identify which AI-102 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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