Quick Answer
The correct answer is to convert the model to TensorFlow and use the Azure IoT Edge Deep Learning module with hardware acceleration. This approach reduces inference latency for custom vision on IoT Edge by leveraging optimized runtime environments like Intel OpenVINO, which offload computation to specialized hardware such as VPUs or FPGAs, bypassing the slower CPU-only processing. On the AI-102 exam, this scenario tests your understanding of edge deployment trade-offs: cloud inference adds network latency, higher resolution increases processing load, and retraining with more images does not affect runtime speed. A common trap is assuming cloud migration always improves performance, but for real-time defect detection on a manufacturing line, local hardware acceleration is essential. Remember the mnemonic “T-H-A-T” for TensorFlow, Hardware Acceleration, and IoT Edge—the three pillars that cut latency without sacrificing accuracy.
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 deploy a custom vision model for defect detection on a manufacturing line. The model runs on an Azure IoT Edge device. You notice that inference latency is too high for real-time detection. Which action should you take to reduce latency?
Move inference to Azure Functions in the cloud
Why wrong: Cloud inference introduces network latency.
Convert the model to TensorFlow and use the Azure IoT Edge Deep Learning module with hardware acceleration
Hardware acceleration reduces inference time.
Retrain the model with more defect images
Why wrong: Does not reduce inference latency.
Increase the resolution of input images
Why wrong: Higher resolution increases processing time.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Convert the model to TensorFlow and use the Azure IoT Edge Deep Learning module with hardware acceleration
Option C is correct because converting the model to TensorFlow and using the Deep Learning module on IoT Edge with hardware acceleration (e.g., Intel OpenVINO) can significantly reduce latency. Option A is wrong because moving to the cloud increases network latency. Option B is wrong because increasing image resolution increases processing time. Option D is wrong because retraining with more images does not reduce inference latency.
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.
Move inference to Azure Functions in the cloud
Why it's wrong here
Cloud inference introduces network latency.
Convert the model to TensorFlow and use the Azure IoT Edge Deep Learning module with hardware acceleration
Why this is correct
Hardware acceleration reduces inference time.
Related concept
Read the scenario before looking for a memorised answer.
Retrain the model with more defect images
Why it's wrong here
Does not reduce inference latency.
Increase the resolution of input images
Why it's wrong here
Higher resolution increases processing time.
Common exam traps
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
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.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A healthcare organisation deploys an application with a public-facing web tier and a private database tier. The database subnet has no public IP and only accepts connections from the web tier's security group. Questions like this test whether you can design cloud network isolation using VNets/VPCs, subnets, and security group rules.
What to study next
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.
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FAQ
Implement computer vision solutions — This question tests Implement computer vision solutions — Read the scenario before looking for a memorised answer..
The correct answer is: Convert the model to TensorFlow and use the Azure IoT Edge Deep Learning module with hardware acceleration — Option C is correct because converting the model to TensorFlow and using the Deep Learning module on IoT Edge with hardware acceleration (e.g., Intel OpenVINO) can significantly reduce latency. Option A is wrong because moving to the cloud increases network latency. Option B is wrong because increasing image resolution increases processing time. Option D is wrong because retraining with more images does not reduce inference latency.
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.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 20, 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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