The correct first step is to retrain the custom object detection model with labeled images of the new products. This is because a pre-built or previously trained model has no knowledge of object classes it has never seen; retraining updates the model’s weights to recognize the new product line through supervised learning on fresh, annotated data. On the Microsoft Azure AI Engineer Associate AI-102 exam, this scenario tests your understanding of the distinction between pre-built Computer Vision models (which are fixed) and custom models built with Custom Vision or the Object Detection API. A common trap is to assume adjusting confidence thresholds or using a different pre-built model will add new classes—neither can introduce unseen object categories. Remember the key principle: to detect a new class, you must provide labeled examples of that class during training. A useful memory tip is “no data, no detect”—if the model hasn’t seen it, it can’t recognize it.
AI-102 Practice Question: Implement image and video processing solutions
This AI-102 practice question tests your understanding of implement image and video processing solutions. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 retail company uses Azure Computer Vision to analyze in-store camera feeds. They recently added a new product line and updated the object detection model. However, the model fails to detect the new products. What should the company do first?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "first"
Why it matters: Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Retrain the custom object detection model with images of the new products.
The model fails to detect new products because it was never trained on them. Retraining the custom object detection model with labeled images of the new products is the correct first step, as it updates the model's knowledge to recognize the new product line. Pre-built models or threshold adjustments cannot add new object classes.
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 the pre-built 'products' model from Computer Vision.
Why it's wrong here
Pre-built models may not include the new product line.
✗
Increase the confidence threshold in the API call.
Why it's wrong here
Adjusting threshold does not add new objects to detect.
✓
Retrain the custom object detection model with images of the new products.
Why this is correct
Custom models need retraining with new labeled data.
Clue confirmation
The clue word "first" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Recreate the Computer Vision resource in a different region.
Why it's wrong here
Region change does not affect model capabilities.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may assume a pre-built model or a simple threshold tweak can handle new object classes, when in fact custom object detection requires retraining with labeled examples of the new items.
Detailed technical explanation
How to think about this question
Custom Vision models use transfer learning from a base CNN (e.g., ResNet) and require retraining with new labeled images to learn novel classes. The training process updates the final classification layer's weights; without this step, the model's output logits have no representation for the new products. In practice, you would collect at least 15–50 images per new product, tag them, and run a new training iteration via the Custom Vision API or portal.
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.
TExam Day Tips
→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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Implement image and video processing solutions — This question tests Implement image and video processing solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Retrain the custom object detection model with images of the new products. — The model fails to detect new products because it was never trained on them. Retraining the custom object detection model with labeled images of the new products is the correct first step, as it updates the model's knowledge to recognize the new product line. Pre-built models or threshold adjustments cannot add new object classes.
What should I do if I get this AI-102 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "first". Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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