The answer is to use the Dense Captioning feature of the Computer Vision Image Analysis API. Unlike standard captioning, which generates a single, often generic sentence, Dense Captioning analyzes an image at a granular level, identifying multiple regions and producing a detailed, descriptive caption for each one—such as “a red car parked next to a green mailbox” instead of just “a car.” This directly addresses the need to improve caption descriptiveness in Image Analysis by providing richer, context-aware output. On the Microsoft Azure AI Engineer Associate AI-102 exam, this question tests your understanding of the distinct capabilities within the Image Analysis service, often trapping candidates who confuse object detection (which returns tags) with captioning, or who mistakenly think raising the confidence threshold adds detail. Remember the memory tip: “Dense equals detailed”—if you need more specific descriptions, think dense, not generic.
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 company uses the Computer Vision Image Analysis API to generate captions for images. The captions are often too generic. How can they improve the descriptiveness of captions?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use the Dense Captioning feature.
Option C is correct because using the 'dense' captioning feature provides more detailed descriptions. Option A is wrong because increasing confidence threshold reduces false positives but does not improve descriptiveness. Option B is wrong because object detection provides tags, not captions. Option D is wrong because Custom Vision can be trained for image classification, not captioning.
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 Object Detection API instead.
Why it's wrong here
Object detection returns bounding boxes and tags, not descriptions.
✗
Train a custom model with Custom Vision.
Why it's wrong here
Custom Vision does not support caption generation natively.
✗
Increase the confidence threshold for captions.
Why it's wrong here
Higher threshold may reduce false positives but does not add detail.
✓
Use the Dense Captioning feature.
Why this is correct
Dense captioning generates more detailed descriptions for regions of the image.
Related concept
Read the scenario before looking for a memorised answer.
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 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 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: Use the Dense Captioning feature. — Option C is correct because using the 'dense' captioning feature provides more detailed descriptions. Option A is wrong because increasing confidence threshold reduces false positives but does not improve descriptiveness. Option B is wrong because object detection provides tags, not captions. Option D is wrong because Custom Vision can be trained for image classification, not captioning.
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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