AI-102 Plan and manage an Azure AI solution Practice Question
Your company deploys an Azure AI Vision solution to detect defects on a manufacturing assembly line. The solution uses a custom object detection model trained on images of products. The model is deployed as a real-time endpoint on an Azure Kubernetes Service (AKS) cluster. Recently, the defect detection accuracy dropped significantly. You suspect data drift because the lighting conditions on the assembly line changed after maintenance. You need to monitor and retrain the model to maintain accuracy. The solution must use Azure AI Foundry's model monitoring capabilities. You also need to automate retraining when drift is detected. What should you do?
⚠ Common exam trap
Many candidates confuse operational scaling (increasing replicas) with model performance improvement, or assume manual retraining is sufficient when the question explicitly requires automated monitoring and retraining using Azure AI Foundry's capabilities.
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
✓
Enable model monitoring in Azure AI Foundry, set up drift detection alerts, and create an automated retraining pipeline
Azure AI Foundry's model monitoring provides built-in drift detection capabilities that can automatically monitor input data distributions and trigger alerts when drift is detected. By combining this with an automated retraining pipeline (e.g., using Azure Machine Learning pipelines or Azure DevOps), you can retrain the custom object detection model on new data reflecting the changed lighting conditions without manual intervention, ensuring sustained accuracy.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Collect more training data from the new lighting conditions and retrain once
Why it's wrong here
Single retraining does not address ongoing drift.
- ✗
Manually review the model performance weekly and retrain if needed
Why it's wrong here
Not automated; may miss drift between reviews.
- ✓
Enable model monitoring in Azure AI Foundry, set up drift detection alerts, and create an automated retraining pipeline
Why this is correct
Continuous monitoring and automated retraining.
- ✗
Increase the number of replicas in the AKS cluster
Why it's wrong here
Scaling does not improve model accuracy.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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