- A
Use Video Indexer's face detection and indexing, then search
Video Indexer indexes faces efficiently and allows search without reprocessing.
- B
Extract keyframes and use Custom Vision to detect the person
Why wrong: Custom Vision requires training and may not be as efficient as Video Indexer's built-in face models.
- C
Run face detection on every frame using Azure AI Face and store results
Why wrong: Processing every frame is costly and high latency.
- D
Use Azure AI Vision to detect faces in video frames and compare against a database
Why wrong: This approach is custom and less efficient than Video Indexer's integrated solution.
Question 605 of 993
Detect Specific Person in Archived Video with Video Indexer
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.
You have a real-time video processing pipeline using Azure AI Video Indexer. You need to detect when a specific person appears in archived video footage. Which approach minimizes latency and cost?
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
✓
Use Video Indexer's face detection and indexing, then search
Video Indexer's built-in face detection and indexing automatically identifies and tracks faces during the indexing process, storing the results in a searchable metadata index. To detect when a specific person appears, you can then search the indexed metadata for that person's face ID or name, which avoids re-processing the video and minimizes both latency and cost. This approach leverages the one-time indexing cost and optimized search capabilities rather than running additional AI services on every frame.
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.
- ✓
Use Video Indexer's face detection and indexing, then search
Why this is correct
Video Indexer indexes faces efficiently and allows search without reprocessing.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Extract keyframes and use Custom Vision to detect the person
Why it's wrong here
Custom Vision requires training and may not be as efficient as Video Indexer's built-in face models.
- ✗
Run face detection on every frame using Azure AI Face and store results
Why it's wrong here
Processing every frame is costly and high latency.
- ✗
Use Azure AI Vision to detect faces in video frames and compare against a database
Why it's wrong here
This approach is custom and less efficient than Video Indexer's integrated solution.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume Custom Vision or Azure AI Face are needed for custom person detection, overlooking that Video Indexer already provides built-in face detection and search capabilities that are optimized for archived video analysis.
Detailed technical explanation
How to think about this question
Video Indexer uses a combination of computer vision and machine learning models to detect, track, and recognize faces across video frames, storing face embeddings and timestamps in its index. When you search for a specific person, the service performs a similarity search against the indexed embeddings rather than re-analyzing the video, which is why it minimizes latency. In a real-world scenario, if you have thousands of hours of archived footage, the one-time indexing cost is amortized, and subsequent searches are near-instantaneous, whereas frame-by-frame approaches would scale linearly with video duration.
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
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
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FAQ
Questions learners often ask
What does this AI-102 question test?
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: Use Video Indexer's face detection and indexing, then search — Video Indexer's built-in face detection and indexing automatically identifies and tracks faces during the indexing process, storing the results in a searchable metadata index. To detect when a specific person appears, you can then search the indexed metadata for that person's face ID or name, which avoids re-processing the video and minimizes both latency and cost. This approach leverages the one-time indexing cost and optimized search capabilities rather than running additional AI services on every frame.
What should I do if I get this AI-102 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: Jul 4, 2026
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