AI-900 Practice Question: Describe features of computer vision workloads on Azure
A robotic arm in a factory needs to pick parts from a bin. The system must identify each part and its exact outline to ensure precise grasping. Which Computer Vision capability should be used?
⚠ Common exam trap
Many candidates confuse object detection (bounding boxes) with semantic segmentation (pixel-level masks), especially when the question emphasizes 'exact outline' — they may incorrectly choose object detection thinking it provides sufficient location information.
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
✓
Semantic segmentation
Semantic segmentation is the correct capability because it classifies each pixel in an image, providing a precise outline of each part. This pixel-level classification is essential for a robotic arm to determine the exact shape and boundaries of parts for accurate grasping, unlike object detection which only provides bounding boxes.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Object detection
Why it's wrong here
Object detection localizes parts by drawing a rectangular bounding box and a class label around each detected item, but it does not estimate the object's actual boundary at the pixel level. For a robotic arm, a bounding box can contain large empty regions or portions of neighboring parts, so the computed center of the box may not correspond to the part's true centroid or stable grasping region. Without fine contour information, the gripper risks missing the part or colliding with adjacent objects.
When this WOULD be correct
A question asking which capability to use for counting parts in a bin or locating their positions for a pick-and-place robot where exact outline is not needed.
- ✗
Image classification
Why it's wrong here
Image classification assigns a single class label to the entire input image, effectively answering 'what is the dominant category in this scene?' while discarding spatial location. In a bin containing multiple parts, the model might output 'part' as the top label but cannot identify each individual object or its pixel coordinates, so the robot has no way to target a specific part. It also cannot distinguish between two different part types present simultaneously.
When this WOULD be correct
A question asking: 'Which Computer Vision capability should be used to determine whether a specific type of part is present in a bin?' would make image classification correct, as it only needs to classify the whole image content.
- ✓
Semantic segmentation
Why this is correct
Semantic segmentation performs dense, pixel-level labeling: every pixel in the image is assigned a class such as 'metal_part', creating a precise silhouette of each part even when parts touch or overlap. This per-pixel contour lets the robot compute the part's exact center of mass, orientation, and stable grasp points, which is exactly what a robotic arm needs when picking parts from a cluttered bin. Unlike coarse bounding boxes or whole-image labels, the mask preserves the true shape and boundary, enabling reliable 3D grasp planning.
- ✗
Optical Character Recognition
Why it's wrong here
Optical Character Recognition (OCR) is designed to detect and decode text glyphs, such as part numbers or serial numbers, converting pixel patterns of characters into machine-readable strings. It provides no measure of the part's geometric extent, surface, or pose, which are the properties needed to position a gripper. Even if the bin has a label, OCR tells the robot which part it is looking at, not where or how to pick it up.
When this WOULD be correct
OCR would be correct in a scenario where the system must read serial numbers or labels printed on parts to identify them, such as reading barcodes or text on packages in a warehouse.
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.
✓Semantic segmentationCorrect answer▾
Why this is correct
Semantic segmentation performs dense, pixel-level labeling: every pixel in the image is assigned a class such as 'metal_part', creating a precise silhouette of each part even when parts touch or overlap. This per-pixel contour lets the robot compute the part's exact center of mass, orientation, and stable grasp points, which is exactly what a robotic arm needs when picking parts from a cluttered bin. Unlike coarse bounding boxes or whole-image labels, the mask preserves the true shape and boundary, enabling reliable 3D grasp planning.
✗Object detectionWrong answer — click to see why▾
Why this is wrong here
Object detection provides bounding boxes around objects but does not produce pixel-level outlines, which are required for precise grasping of irregularly shaped parts.
★ When this WOULD be the correct answer
A question asking which capability to use for counting parts in a bin or locating their positions for a pick-and-place robot where exact outline is not needed.
Why candidates choose this
Candidates may confuse object detection with segmentation, assuming that detecting an object's bounding box is sufficient for grasping, without realizing the need for precise outline.
✗Image classificationWrong answer — click to see why▾
Why this is wrong here
Image classification assigns a single label to the entire image, but the question requires identifying each part and its exact outline for precise grasping, which needs pixel-level segmentation.
★ When this WOULD be the correct answer
A question asking: 'Which Computer Vision capability should be used to determine whether a specific type of part is present in a bin?' would make image classification correct, as it only needs to classify the whole image content.
Why candidates choose this
Candidates may confuse image classification with object detection or segmentation, thinking that identifying the part type is sufficient, without realizing the need for exact outline localization.
✗Optical Character RecognitionWrong answer — click to see why▾
Why this is wrong here
Optical Character Recognition (OCR) is designed to extract text from images, not to identify parts or their outlines. The question requires identifying parts and their exact outlines for grasping, which is a segmentation task, not text recognition.
★ When this WOULD be the correct answer
OCR would be correct in a scenario where the system must read serial numbers or labels printed on parts to identify them, such as reading barcodes or text on packages in a warehouse.
Why candidates choose this
Candidates might confuse OCR with general object recognition, thinking that 'recognizing' parts includes reading any identifying marks, but OCR specifically handles text, not object shapes or outlines.
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?”
Go deeper
Related to this question
Learn chapter
Regression and Classification
Key term
Object detection
Object detection is a computer vision technology that identifies and locates specific objects within an image or video.
Key term
Classification
Classification is a supervised machine learning technique used to predict a category or class label for new data based on patterns learned from labeled training data.
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