AI-900 Practice Question: Describe features of computer vision workloads on Azure
A digital art library wants to automatically generate a list of relevant keywords (e.g., 'landscape', 'portrait', 'abstract', 'nature') for each image in their collection. Which Azure Computer Vision capability should they use?
⚠ Common exam trap
Many exam-takers confuse Image Captioning (which produces a single sentence) with Image Tagging (which produces a list of keywords), or they assume Object Detection is needed because it identifies objects, but it does not return a simple keyword list.
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
✓
Image Tagging
Image Tagging (B) is the correct capability because it analyzes the content of an image and returns a set of relevant keywords (tags) based on the detected objects, scenes, and concepts. This directly matches the requirement to generate a list of keywords like 'landscape', 'portrait', 'abstract', and 'nature' for each image.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Optical Character Recognition (OCR)
Why it's wrong here
Optical Character Recognition (OCR) extracts printed or handwritten text from an image, returning the recognized characters and their bounding box coordinates. It does not infer descriptive keywords about the image's visual content; it only reads language characters that are physically present. For example, OCR would output a sign's wording but never a tag like 'outdoor' or 'bicycle.' Therefore it cannot produce a general list of image keywords.
When this WOULD be correct
A question asking to extract printed or handwritten text from scanned documents or images, such as digitizing receipts or forms.
- ✓
Image Tagging
Why this is correct
Image Tagging is the correct service because Azure Computer Vision's Tag feature analyzes the visual content and returns a list of descriptive keywords or tags—such as 'tree,' 'outdoor,' and 'person'—each with a confidence score. It is designed precisely for generating metadata that describes the image content in a non-detailed, multi-tag format. Unlike OCR or object detection, it does not focus on text or spatial localization; it produces a broad semantic tag list.
- ✗
Image Captioning
Why it's wrong here
Image Captioning generates a single, natural-language sentence summarizing the whole image, such as 'A woman is walking her dog in the park.' It is a sequential text-generation task, not a keyword extraction task, so it does not output a list of discrete tags. While the caption may contain some keywords, the result is a grammatical sentence rather than an enumerable set of descriptors. Therefore it is not the right choice for automatically generating a list of relevant keywords.
When this WOULD be correct
A question asks: 'A museum wants to automatically generate a descriptive sentence for each artwork in their collection to aid visually impaired visitors. Which Azure Computer Vision capability should they use?'
- ✗
Object Detection
Why it's wrong here
Object Detection identifies and localizes specific instances of objects (like a dog, car, or person) by drawing bounding boxes around each occurrence and labeling the object class. It provides positional and spatial context, not a holistic list of keywords for the entire image. If an image contains a person and a bicycle, object detection might return two boxes with labels, but it would not generate additional descriptive tags such as 'sunny' or 'urban.' Thus it fails to meet the requirement for a general keyword list.
When this WOULD be correct
A question asking for identifying and locating specific objects in an image, such as 'Which Azure Computer Vision capability can detect and draw bounding boxes around all instances of 'cat' in an image?'
Option-by-option analysis
Why each answer is right or wrong
Understanding why wrong answers are wrong — and when they would be correct — is what separates a 750 score from a 900. The AI-900 exam frequently reuses these exact scenarios with slightly different constraints.
✓Image TaggingCorrect answer▾
Why this is correct
Image Tagging is the correct service because Azure Computer Vision's Tag feature analyzes the visual content and returns a list of descriptive keywords or tags—such as 'tree,' 'outdoor,' and 'person'—each with a confidence score. It is designed precisely for generating metadata that describes the image content in a non-detailed, multi-tag format. Unlike OCR or object detection, it does not focus on text or spatial localization; it produces a broad semantic tag list.
✗Optical Character Recognition (OCR)Wrong answer — click to see why▾
Why this is wrong here
OCR extracts text from images, not descriptive keywords about image content like 'landscape' or 'abstract'.
★ When this WOULD be the correct answer
A question asking to extract printed or handwritten text from scanned documents or images, such as digitizing receipts or forms.
Why candidates choose this
Candidates may confuse 'keywords' with 'text' and think OCR can generate tags by reading embedded text in images.
✗Image CaptioningWrong answer — click to see why▾
Why this is wrong here
Image Captioning generates a human-readable sentence describing the image, not a list of individual keywords. The question specifically asks for a list of keywords, which is the output of Image Tagging.
★ When this WOULD be the correct answer
A question asks: 'A museum wants to automatically generate a descriptive sentence for each artwork in their collection to aid visually impaired visitors. Which Azure Computer Vision capability should they use?'
Why candidates choose this
Candidates may confuse captioning with tagging because both involve describing image content, but captioning produces a sentence rather than a list of keywords.
✗Object DetectionWrong answer — click to see why▾
Why this is wrong here
Object Detection identifies and locates objects within an image (e.g., 'dog', 'car'), but does not generate thematic keywords like 'landscape' or 'abstract' that describe the image's style or genre.
★ When this WOULD be the correct answer
A question asking for identifying and locating specific objects in an image, such as 'Which Azure Computer Vision capability can detect and draw bounding boxes around all instances of 'cat' in an image?'
Why candidates choose this
Candidates may confuse object detection with image tagging, thinking that detecting objects inherently provides keywords, but object detection focuses on spatial location rather than thematic labeling.
Analysis generated from the official AI-900blueprint and verified against question context. The “when correct” sections are what AI assistants cite when candidates ask “what’s the difference between these options?”
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI-900 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-900 exam.