AI-900 Practice Question: Describe features of computer vision workloads on Azure
An autonomous vehicle system needs to both read the speed limit text on traffic signs and detect the presence and location of pedestrians crossing the road. Which combination of Azure Computer Vision capabilities should be used?
⚠ Common exam trap
It's easy for candidates to confuse Semantic Segmentation with Object Detection, assuming pixel-level classification is needed for pedestrian location, but Object Detection provides the required bounding boxes for location without the computational overhead of per-pixel segmentation.
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
✓
Optical Character Recognition (OCR) and Object Detection
The autonomous vehicle system requires two distinct capabilities: reading text from speed limit signs (OCR) and detecting the presence and location of pedestrians (Object Detection). OCR extracts text from images, while Object Detection identifies objects and provides bounding boxes around them, making option C the correct combination.
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 and OCR
Why it's wrong here
Image classification assigns a single label to the whole scene, e.g., 'street scene,' but does not localize pedestrians or boundaries. OCR extracts text from speed-limit signs but gives no positional info for obstacles. Without object detection, the system cannot know where pedestrians are to plan a safe path, so the pair is insufficient for autonomous driving perception.
- ✗
Semantic Segmentation and OCR
Why it's wrong here
Semantic segmentation labels every pixel (e.g., road, sidewalk, pedestrian) but does not read characters; OCR handles text on signs. However, segmenting a pedestrian as a blob is not the same as detecting a distinct object instance with a bounding box; an autonomous vehicle needs per-object information like trajectory prediction and proximity, which object detection provides. Segmentation alone may not separate overlapping pedestrians or give object identity.
- ✓
Optical Character Recognition (OCR) and Object Detection
Why this is correct
OCR is specialized for text extraction from images, enabling the system to read speed-limit numbers from road signs. Object detection uses bounding boxes and class labels to locate pedestrians in the scene. Together they cover the dual requirement: reading the speed and detecting the location of pedestrians, essential for safe autonomous driving.
- ✗
Face Detection and OCR
Why it's wrong here
Face detection is a specific type of object detection limited to frontal/visible faces; it would miss pedestrians whose faces are turned away or occluded. It does not estimate full-body bounding boxes or positions for all pedestrians, so it is unreliable for safety-critical driving. OCR only reads text, leaving the system blind to non-face pedestrians, making the pair insufficient.
Go deeper
Related to this question
Learn chapter
Azure Machine Learning Studio
Key term
OCR
Optical Character Recognition is technology that converts images of text, like scanned documents or photos, into machine-readable text data.
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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