Quick Answer
The answer is to set a high minimum confidence threshold for detection. This configuration directly reduces latency on IoT Edge with Video Analyzer by filtering out low-confidence predictions, which minimizes the number of false positives that the edge device must process and transmit over limited network bandwidth. By raising the threshold, you offload unnecessary computational work from the constrained edge hardware, ensuring only high-confidence, actionable intrusion alerts are sent to the cloud. On the Microsoft Azure AI Engineer Associate AI-102 exam, this scenario tests your understanding of how to optimize real-time video analytics at the edge, often appearing as a trick where candidates mistakenly choose to increase frame rate or resolution—both of which would worsen latency. The core concept is that confidence thresholds control event volume, not video quality. Memory tip: think "High threshold, low latency"—like a bouncer letting only the most certain guests through the door.
This AI-102 practice question tests your understanding of implement image and video processing solutions. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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.
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?
Increase the video resolution sent to the cloud.
Why wrong: Higher resolution increases latency.
Set a high minimum confidence threshold for detection.
Filters out low-confidence results, saving compute.
Enable cloud-based processing for all frames.
Why wrong: Adds network latency.
Use multiple AI models simultaneously.
Why wrong: Increases compute load.
Answer choices
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.
Option B is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
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.
Related concept
Read the scenario before looking for a memorised answer.
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.
Common exam traps
The trap here is that candidates often 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.
Detailed technical explanation
Azure Video Analyzer on IoT Edge uses a pipeline topology where AI inference modules (e.g., from Azure Cognitive Services) run locally on the edge device. By configuring a high minimum confidence threshold (e.g., 0.8 or 0.9), the system filters out low-confidence detections before they are sent to the cloud or trigger alerts, effectively reducing the number of events that must be processed by downstream analytics or stored. This is particularly important in scenarios like perimeter intrusion detection, where false positives from motion or animals could overwhelm the system if not filtered at the edge.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
Implement image and video processing solutions — This question tests Implement image and video processing solutions — Read the scenario before looking for a memorised answer..
The correct answer is: Set a high minimum confidence threshold for detection. — Option B is correct because 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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 11, 2026
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.
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