Quick Answer
The correct answer is to retrain the model with new images captured from the current camera angle. This is necessary because the accuracy drop is caused by a domain shift—a change in the data distribution between the training set (original camera angle) and the inference environment (new angle). In Azure Custom Vision, even slight changes in perspective, lighting, or background can degrade performance, as the model learns visual features specific to the training domain. On the AI-102 exam, this scenario tests your understanding of domain shift and the retraining workflow for edge-deployed models; a common trap is to assume the issue is hardware-related (low memory/CPU) and try to optimize the container instead of addressing the data mismatch. Remember the memory tip: "Angle shift? Retrain the gift."—when the camera angle changes, retraining with fresh images from the new perspective is the direct fix, not hardware tuning.
This AI-102 practice question tests your understanding of plan and manage an azure ai solution. 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 are an Azure AI engineer at Fabrikam Inc. The company has developed a custom vision model using Azure Custom Vision to detect defects on a manufacturing assembly line. The model is deployed as a Docker container to an on-premises edge device using Azure IoT Edge. Recently, the model's inference accuracy has decreased. The operations team reports that the edge device is running low on memory and CPU. The model was trained with images from a specific camera angle, but the camera angle has been changed slightly due to maintenance. You need to improve the model's accuracy. What should you do?
Upgrade the edge device to have more memory and CPU.
Why wrong: Resources affect performance, not accuracy.
Reduce the image resolution to lower memory usage.
Why wrong: Lower resolution may reduce accuracy.
Retrain the model with new images captured from the current camera angle.
The model needs to learn the new perspective.
Convert the model to use grayscale images.
Why wrong: Grayscale may lose important color features.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Retrain the model with new images captured from the current camera angle.
The decrease in accuracy is most likely due to the change in camera angle, which introduces a domain shift between the training images and the new inference images. Retraining the model with images captured from the current camera angle will realign the training data distribution with the production environment, directly addressing the root cause of the accuracy drop. This is a standard practice in Custom Vision when deployment conditions change.
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.
Upgrade the edge device to have more memory and CPU.
Why it's wrong here
Resources affect performance, not accuracy.
Reduce the image resolution to lower memory usage.
Why it's wrong here
Lower resolution may reduce accuracy.
Retrain the model with new images captured from the current camera angle.
Why this is correct
The model needs to learn the new perspective.
Related concept
Read the scenario before looking for a memorised answer.
Convert the model to use grayscale images.
Why it's wrong here
Grayscale may lose important color features.
Common exam traps
The trap here is that candidates focus on the resource constraints (low memory/CPU) as the primary cause of accuracy loss, but the question explicitly states the camera angle changed, making retraining the only option that addresses the domain shift.
Detailed technical explanation
Azure Custom Vision models are sensitive to changes in the input image distribution, including camera angle, lighting, and background. The model's convolutional layers learn spatial features specific to the training data; a slight angle shift can cause feature mismatches in the learned filters. Retraining with new images (ideally 15–30 per class) using the Custom Vision portal or API triggers transfer learning, adjusting the model weights to the new domain while preserving previously learned features.
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
Plan and manage an Azure AI solution — This question tests Plan and manage an Azure AI solution — Read the scenario before looking for a memorised answer..
The correct answer is: Retrain the model with new images captured from the current camera angle. — The decrease in accuracy is most likely due to the change in camera angle, which introduces a domain shift between the training images and the new inference images. Retraining the model with images captured from the current camera angle will realign the training data distribution with the production environment, directly addressing the root cause of the accuracy drop. This is a standard practice in Custom Vision when deployment conditions change.
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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