AI-900 Practice Question: Describe features of computer vision workloads on Azure
What is image classification and how is it different from object detection?
⚠ Common exam trap
It's easy for candidates to confuse the output granularity—thinking object detection is just a 'more detailed' version of classification rather than a fundamentally different task with spatial localization, leading them to choose Option B or D.
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 classification labels the whole image; object detection finds and locates multiple objects within it
Image classification assigns a single label to an entire image based on its dominant content, such as 'cat' or 'dog'. Object detection goes further by not only identifying multiple objects within an image but also drawing bounding boxes around each one, providing both class labels and spatial locations. This distinction is fundamental in computer vision workloads on Azure, where Custom Vision and Computer Vision API offer separate capabilities for classification and detection tasks.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Image classification labels the whole image; object detection finds and locates multiple objects within it
Why this is correct
This option is correct. Image classification evaluates the whole image as a single unit and outputs one label (e.g., 'cat' for a photo containing a cat, even if other objects exist). Object detection goes further by finding multiple objects in the image and returning for each one a class label and a bounding box that specifies its location (e.g., 'cat at (x1,y1,x2,y2) and dog at (x3,y3,x4,y4)'). Thus the core difference is the granularity of the output: a single global label versus localized, per-object predictions.
- ✗
Image classification is faster; object detection is slower but more accurate
Why it's wrong here
The question asks for the conceptual difference between image classification and object detection, not a performance comparison. Image classification assigns a single label to an entire image, whereas object detection localises multiple instances with bounding boxes. This option is tempting because object detection’s additional localisation step does require more computation, making it slower in practice, but speed versus accuracy is not the defining axis the question demands.
- ✗
Image classification works on videos; object detection works on static images only
Why it's wrong here
This option is incorrect because the medium (video versus static images) does not define the difference between image classification and object detection. Both tasks can be applied to individual frames extracted from video or to static images; the true distinction lies in the output, not the input source. Image classification produces a single label for the entire frame, whereas object detection produces multiple object instances, each with a class label and a bounding box.
- ✗
They are the same task with different names
Why it's wrong here
This option is incorrect because image classification and object detection are conceptually and technically different tasks. Image classification assigns one image-level categorical label to the entire image, representing the dominant subject or scene. Object detection must both identify which objects are present and localize each one with a bounding box, outputting multiple label-and-coordinate pairs; it combines classification with localization, making it fundamentally different.
Go deeper
Related to this question
Learn chapter
Types of AI Workloads
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.
Key term
Object detection
Object detection is a computer vision technology that identifies and locates specific objects within an image or video.
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