The correct answer is to periodically collect new images with labels, retrain the model in Custom Vision, and redeploy the updated container. This is necessary because Azure Custom Vision lacks built-in drift detection or automatic retraining capabilities; when data drift occurs—such as changes in lighting or product appearance on an assembly line—the model’s accuracy degrades, and the only way to recover is by manually capturing representative new data, retraining the model in the cloud, and redeploying the updated container to the edge server. On the AI-102 exam, this question tests your understanding of Custom Vision’s limitations versus Azure Machine Learning’s capabilities, often appearing as a trap where candidates assume automated drift monitoring exists. A common memory tip is “Custom Vision is custom work—no auto-detect, no auto-retrain,” reminding you that you must implement your own monitoring loop.
AI-102 Implement computer vision solutions Practice Question
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.
A manufacturing company uses Azure Custom Vision to detect defects on an assembly line. The model is deployed to a container on a local edge server. Recently, the model's accuracy dropped. You suspect data drift. What should you do to monitor and retrain the model?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Periodically collect new images with labels, retrain the model in Custom Vision, and redeploy the updated container.
Option B is correct because Custom Vision does not have built-in drift detection, so you must capture new images and labels, then retrain the model in the cloud and redeploy the updated container. Option A is wrong because Custom Vision does not provide automatic retraining based on feedback. Option C is wrong because Azure Machine Learning is not directly integrated with Custom Vision for this purpose. Option D is wrong because there is no built-in drift detection; you must implement custom monitoring.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Use Azure Machine Learning data drift monitoring on the Custom Vision endpoint.
Why it's wrong here
Azure ML drift monitoring is not natively integrated with Custom Vision.
✓
Periodically collect new images with labels, retrain the model in Custom Vision, and redeploy the updated container.
Why this is correct
Manual retraining is required to address drift.
Related concept
Read the scenario before looking for a memorised answer.
✗
Configure Custom Vision to send alerts when drift is detected.
Why it's wrong here
Custom Vision does not have drift detection.
✗
Enable active learning in Custom Vision to automatically retrain the model.
Why it's wrong here
Active learning helps with labeling, not automatic retraining.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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
How to think about this question
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.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
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.
Implement computer vision solutions — This question tests Implement computer vision solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Periodically collect new images with labels, retrain the model in Custom Vision, and redeploy the updated container. — Option B is correct because Custom Vision does not have built-in drift detection, so you must capture new images and labels, then retrain the model in the cloud and redeploy the updated container. Option A is wrong because Custom Vision does not provide automatic retraining based on feedback. Option C is wrong because Azure Machine Learning is not directly integrated with Custom Vision for this purpose. Option D is wrong because there is no built-in drift detection; you must implement custom monitoring.
What should I do if I get this AI-102 question wrong?
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A manufacturing company uses Azure AI Custom Vision to detect defects on a production line. The model was trained with 500 images per class and achieves 95% accuracy. After deployment, the model's accuracy drops to 80% due to changes in lighting conditions. What is the most effective first step to improve the model's robustness?
hard
A.Reduce the probability threshold to increase recall.
✓ B.Capture additional images under the new lighting and retrain the model.
C.Use Azure AutoML to automatically find the best algorithm.
D.Add more images from the original lighting conditions to the training set.
Why B: Option C is correct because retraining with images that represent the new lighting conditions will directly address the distribution shift. Adding more images of the original lighting (A) won't help with new conditions. Adjusting probability threshold (B) might change precision/recall but not robustness. Using AutoML (D) is not directly applicable to Custom Vision.
Last reviewed: Jun 20, 2026
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