- A
Captioning adds user-written descriptions; tagging uses AI to detect objects automatically
Why wrong: Both are AI-generated — captioning produces a natural language sentence while tagging returns individual concept keywords.
- B
Captioning generates a natural language sentence describing the scene; tagging returns individual concept keywords
Caption: 'A cat sitting on a sofa.' Tags: ['cat', 'sofa', 'indoor']. Captions provide narrative context; tags enable efficient filtering.
- C
Captioning works on video; tagging works only on still images
Why wrong: Both can work on images — the distinction is output format: sentence vs. keyword list, not media type.
- D
Image tagging is more accurate than captioning because it uses simpler classification
Why wrong: Accuracy depends on the task — captions are grammatically generated sentences while tags are discrete labels; they serve different purposes.
Quick Answer
The correct distinction is that image captioning generates a natural language sentence describing the scene, while image tagging returns individual concept keywords. This difference stems from how each feature processes visual data: captioning uses a deep learning model to analyze the entire image holistically and produce a coherent, descriptive sentence like "a group of people playing soccer in a park," whereas tagging identifies discrete objects, actions, or attributes and outputs them as a list of keywords such as "soccer," "grass," and "people." On the AI-900 exam, this question tests your understanding of the specific outputs each Azure AI Vision feature provides, and a common trap is confusing tagging with captioning because both involve recognizing content. To remember, think of captioning as writing a full sentence for a story, while tagging is just listing the nouns and verbs.
AI-900 Practice Question: Describe features of computer vision workloads on Azure
This AI-900 practice question tests your understanding of describe features of computer vision workloads on azure. 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.
What is 'image captioning' in Azure AI Vision and how is it different from image tagging?
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
Captioning generates a natural language sentence describing the scene; tagging returns individual concept keywords
Option B is correct because image captioning in Azure AI Vision uses a deep learning model to analyze the entire scene and generate a coherent, natural language sentence describing the image content, such as 'a group of people playing soccer in a park.' In contrast, image tagging returns a list of individual keywords or concepts (e.g., 'soccer,' 'grass,' 'people') without forming a complete sentence. This distinction is fundamental to understanding the different outputs of these two Azure AI Vision features.
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.
- ✗
Captioning adds user-written descriptions; tagging uses AI to detect objects automatically
Why it's wrong here
Both are AI-generated — captioning produces a natural language sentence while tagging returns individual concept keywords.
- ✓
Captioning generates a natural language sentence describing the scene; tagging returns individual concept keywords
Why this is correct
Caption: 'A cat sitting on a sofa.' Tags: ['cat', 'sofa', 'indoor']. Captions provide narrative context; tags enable efficient filtering.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Captioning works on video; tagging works only on still images
Why it's wrong here
Both can work on images — the distinction is output format: sentence vs. keyword list, not media type.
- ✗
Image tagging is more accurate than captioning because it uses simpler classification
Why it's wrong here
Accuracy depends on the task — captions are grammatically generated sentences while tags are discrete labels; they serve different purposes.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse image captioning with manual annotation or assume tagging is always more accurate, when in fact the key difference is the output format—a full sentence versus a list of keywords—not the method of input or accuracy level.
Trap categories for this question
Keyword trap
Both are AI-generated — captioning produces a natural language sentence while tagging returns individual concept keywords.
Command / output trap
Both can work on images — the distinction is output format: sentence vs. keyword list, not media type.
Detailed technical explanation
How to think about this question
Under the hood, Azure AI Vision's image captioning leverages a convolutional neural network (CNN) for feature extraction combined with a recurrent neural network (RNN) or transformer-based language model to generate sequential text. The model is trained on large datasets like COCO (Common Objects in Context) to learn associations between visual features and natural language phrases. A subtle behavior is that captioning can sometimes produce generic descriptions (e.g., 'a person is sitting') if the scene lacks distinctive objects, whereas tagging always returns specific concept keywords with confidence scores.
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
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FAQ
Questions learners often ask
What does this AI-900 question test?
Describe features of computer vision workloads on Azure — This question tests Describe features of computer vision workloads on Azure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Captioning generates a natural language sentence describing the scene; tagging returns individual concept keywords — Option B is correct because image captioning in Azure AI Vision uses a deep learning model to analyze the entire scene and generate a coherent, natural language sentence describing the image content, such as 'a group of people playing soccer in a park.' In contrast, image tagging returns a list of individual keywords or concepts (e.g., 'soccer,' 'grass,' 'people') without forming a complete sentence. This distinction is fundamental to understanding the different outputs of these two Azure AI Vision features.
What should I do if I get this AI-900 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 11, 2026
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