AI-102 Plan and manage an Azure AI solution Practice Question
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?
⚠ Common exam trap
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.
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
✓
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.
Answer analysis
Option-by-option breakdown
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
More memory and CPU address resource exhaustion, not the accuracy loss, which stems from the changed camera angle shifting inference inputs away from the training distribution. It is tempting because the device is genuinely constrained, but hardware scaling would be correct only if throttling, not angle drift, caused the degradation.
- ✗
Reduce the image resolution to lower memory usage.
Why it's wrong here
Lowering resolution removes fine detail the model relies on to spot defects, degrading accuracy further. It is tempting because it eases the reported memory and CPU shortage, but that shortage is not the accuracy cause; the camera-angle shift is, so retraining with new-angle images is needed.
- ✓
Retrain the model with new images captured from the current camera angle.
Why this is correct
The camera angle shift changed the input distribution, so the model now infers on images unlike its training data. Retraining with images captured from the current angle realigns the model with production input, addressing the accuracy drop at its source.
- ✗
Convert the model to use grayscale images.
Why it's wrong here
Grayscale conversion discards colour information the defect detector was trained on, worsening accuracy rather than restoring it. It is tempting because it reduces input size and memory pressure, but the accuracy drop traces to the camera-angle change, so retraining with images from the new angle is required.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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