hardMultiple ChoiceObjective-mapped
AI-102 Practice Question: A security company uses Azure Video Analyzer on…
A security company uses Azure Video Analyzer on IoT Edge to detect intrusions. The edge device has limited compute and network. They need to reduce latency. What should they configure?
⚠ Common exam trap
A common mix-up: candidates assume increasing cloud processing (Option C) or using more models (Option D) improves accuracy, but they overlook the critical constraint of limited compute and network on the edge device, which makes local filtering via confidence thresholds the correct latency-reducing strategy.
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
✓
Set a high minimum confidence threshold for detection.
Setting a high minimum confidence threshold for detection reduces the number of false positives and the volume of events that need to be processed and transmitted. This directly lowers the computational load on the edge device and reduces network bandwidth usage, thereby decreasing latency for actionable intrusion alerts.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the video resolution sent to the cloud.
Why it's wrong here
Higher resolution increases latency.
- ✓
Set a high minimum confidence threshold for detection.
Why this is correct
Filters out low-confidence results, saving compute.
- ✗
Enable cloud-based processing for all frames.
Why it's wrong here
Adds network latency.
- ✗
Use multiple AI models simultaneously.
Why it's wrong here
Increases compute load.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI-102 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-102 exam.