AI-900 Practice Question: Describe features of computer vision workloads on Azure
A real estate agency wants to create a feature on their website that automatically crops uploaded property photos to focus on the house itself, removing excess sky, ground, or other surroundings. Which Azure Computer Vision capability should they use?
⚠ Common exam trap
Test-takers frequently confuse object detection with smart cropping, assuming that detecting the house with a bounding box is equivalent to cropping, but object detection only provides coordinates and does not automatically perform the intelligent, composition-aware cropping that smart cropping does.
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
✓
Smart cropping
Smart cropping is the correct capability because it uses AI to identify the most visually salient region of an image and automatically crops it to focus on the main subject—in this case, the house—while removing irrelevant background like sky or ground. This is distinct from generic cropping as it leverages computer vision to detect the primary object and compositionally frame it.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
OCR (Optical Character Recognition)
Why it's wrong here
Optical Character Recognition extracts text from images, not spatial boundaries of objects like a house. It would fail because the requirement is to isolate a building from its background, not to read printed or handwritten characters. This option tempts because OCR is a well-known Azure Computer Vision feature for processing document images, and it would be correct if the agency needed to extract address text from property signs or listing details.
When this WOULD be correct
A question asking which Azure Computer Vision capability can extract printed or handwritten text from property photos, such as reading a 'For Sale' sign or house number, would make OCR the correct answer.
- ✗
Image captioning
Why it's wrong here
Image captioning produces a human-readable sentence describing the content of a photo, such as 'a single-family home with a white picket fence and two cars in the driveway.' The output is plain text, so it contains no pixel-level spatial coordinates or image transformation logic that could be used to crop the picture. While captions could enhance accessibility or generate listing descriptions, they cannot isolate or reframe the subject of an image.
When this WOULD be correct
A question asking: 'Which Azure Computer Vision capability can automatically generate a human-readable description of a property photo for accessibility purposes?' would make image captioning correct.
- ✓
Smart cropping
Why this is correct
Smart cropping in Azure AI Vision uses a saliency model to identify the most visually interesting region of an uploaded property photo, then returns a cropped version of that region at a requested aspect ratio. Because the crop is driven by visual importance rather than predefined object classes, it naturally centers the main house or a noteworthy architectural detail without requiring a detection model.
- ✗
Object detection
Why it's wrong here
Object detection locates and classifies multiple objects in an image by drawing bounding boxes and labels, such as 'house' or 'garage,' but its output is annotation data rather than an edited image. To build a website cropping feature you would still need to extract the bounding box and perform a separate crop operation, and the result would often contain multiple boxes or miss the best composition. Smart crop is purpose-built for generating the final image, whereas object detection is designed to answer 'what is where.'
When this WOULD be correct
Object detection would be correct if the question asked for identifying the presence and location of houses in an image, such as 'Which capability can detect and draw bounding boxes around houses in uploaded property photos?'
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.
✓Smart croppingCorrect answer▾
Why this is correct
Smart cropping in Azure AI Vision uses a saliency model to identify the most visually interesting region of an uploaded property photo, then returns a cropped version of that region at a requested aspect ratio. Because the crop is driven by visual importance rather than predefined object classes, it naturally centers the main house or a noteworthy architectural detail without requiring a detection model.
✗OCR (Optical Character Recognition)Wrong answer — click to see why▾
Why this is wrong here
OCR is used to extract text from images, not to crop images to focus on a specific subject like a house.
★ When this WOULD be the correct answer
A question asking which Azure Computer Vision capability can extract printed or handwritten text from property photos, such as reading a 'For Sale' sign or house number, would make OCR the correct answer.
Why candidates choose this
Candidates may confuse OCR with image analysis capabilities, thinking it can identify and isolate the house as a 'text-like' object, or they may not understand the specific purpose of smart cropping.
✗Image captioningWrong answer — click to see why▾
Why this is wrong here
Image captioning generates a textual description of an image, not a cropped region. The question requires cropping to focus on the house, which is a spatial transformation, not a description.
★ When this WOULD be the correct answer
A question asking: 'Which Azure Computer Vision capability can automatically generate a human-readable description of a property photo for accessibility purposes?' would make image captioning correct.
Why candidates choose this
Candidates may confuse 'captioning' with 'cropping' because both involve understanding image content, but they serve fundamentally different purposes.
✗Object detectionWrong answer — click to see why▾
Why this is wrong here
Object detection identifies and locates objects within an image (e.g., drawing bounding boxes around houses), but it does not automatically crop the image to focus on a specific object. The required capability is smart cropping, which intelligently crops images to highlight the main subject.
★ When this WOULD be the correct answer
Object detection would be correct if the question asked for identifying the presence and location of houses in an image, such as 'Which capability can detect and draw bounding boxes around houses in uploaded property photos?'
Why candidates choose this
Candidates may confuse object detection with smart cropping because both involve identifying objects; they might think detecting the house is sufficient to crop around it, not realizing cropping requires a separate, specialized capability.
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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Azure Machine Learning Studio
Key term
Feature
A feature is a distinct unit of functionality that delivers value to the user, often managed and tracked throughout the software development lifecycle.
Key term
Computer vision
Computer vision is a field of artificial intelligence that enables computers to interpret and make decisions based on visual data from the world, such as images and videos.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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