The answer is to train a Custom Vision classification model with images labeled using the company’s specific product categories. This approach directly addresses the need to improve tagging accuracy with custom categories because Custom Vision allows you to build a specialized classifier on top of the prebuilt Image Analysis API, learning the visual patterns that distinguish ‘electronics’ from ‘communication device’ without requiring a completely new model from scratch. On the AI-102 exam, this scenario tests your understanding of when to extend Azure’s prebuilt services with a custom model rather than adjusting thresholds or using alternative features like dense captioning, which provide descriptions rather than structured tags. A common trap is assuming that raising the confidence threshold will add new categories—it only filters existing ones. Remember the memory tip: “Prebuilt tags are general; custom tags are specific—train a Vision model to make the switch.”
AI-102 Implement computer vision solutions Practice Question
This AI-102 practice question tests your understanding of implement computer vision solutions. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 the Computer Vision Image Analysis API to generate tags for product images in their e-commerce catalog. They want to automatically tag images with product categories such as 'electronics', 'clothing', and 'home goods'. The prebuilt tags often misclassify items. For example, a smartphone is tagged as 'communication device' instead of 'electronics'. You need to improve the tagging accuracy for the company's specific product categories without building a completely new model. What should you do?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Train a Custom Vision classification model with images labeled with the company's product categories.
Option D is correct because Custom Vision can be trained on the company's product images to produce custom tags that match their categories. Option A is wrong because increasing confidence threshold may reduce false positives but does not add custom categories. Option B is wrong because adding more general tags does not help. Option C is wrong because the dense captioning feature provides descriptions, not structured tags.
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.
✓
Train a Custom Vision classification model with images labeled with the company's product categories.
Why this is correct
Custom Vision can generate custom tags tailored to the company's taxonomy.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use the Dense Captioning feature to generate detailed descriptions and parse them for categories.
Why it's wrong here
Dense captions are not structured tags.
✗
Increase the confidence threshold for tags to reduce false positives.
Why it's wrong here
Higher threshold does not introduce custom category tags.
✗
Use the 'brands' feature to identify product brands and map them to categories.
Why it's wrong here
Brands are not product categories.
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
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
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: Train a Custom Vision classification model with images labeled with the company's product categories. — Option D is correct because Custom Vision can be trained on the company's product images to produce custom tags that match their categories. Option A is wrong because increasing confidence threshold may reduce false positives but does not add custom categories. Option B is wrong because adding more general tags does not help. Option C is wrong because the dense captioning feature provides descriptions, not structured tags.
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.
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Question Discussion
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