AI-900 Practice Question: Describe features of computer vision workloads on Azure
An autonomous driving company is developing a system that needs to understand the road scene at a granular level. For each pixel in a camera image, the system must classify whether it belongs to the road, a pedestrian, a vehicle, a traffic sign, or the sky. Which Azure Computer Vision capability should they use?
⚠ Common exam trap
Test-takers frequently confuse object detection with pixel-level classification, assuming bounding boxes provide enough detail, but semantic segmentation is required for granular scene understanding where every pixel matters.
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 choice because it classifies every pixel in an image into a predefined category, such as road, pedestrian, vehicle, traffic sign, or sky. This pixel-level classification is essential for autonomous driving to understand the road scene at a granular level, enabling precise boundary detection and scene understanding.
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
Why it's wrong here
Image classification assigns a single label (e.g., "sedan") to the entire image, collapsing all spatial information into one global decision. For autonomous driving, this cannot distinguish a pedestrian on the crosswalk from a tree on the sidewalk, let alone outline the road's drivable area. The task lacks the per-pixel granularity required to understand a scene's geometry and obstacle layout.
- ✗
Object detection
Why it's wrong here
Object detection locates instances with bounding boxes (e.g., rectangle around a car) but does not output a mask for each pixel. In driving, boxes are coarse: they overlap the background, miss the exact contour of a pedestrian's arm or a bicycle's wheel, and cannot label free space or road surface. It offers region-level, not pixel-level, scene understanding.
- ✓
Semantic segmentation
Why this is correct
Semantic segmentation classifies every pixel into a predefined class (e.g., road, sidewalk, car, person), producing a dense, pixel-aligned label map. This per-pixel understanding is essential for autonomous driving to precisely identify drivable area, obstacle boundaries, and lane markings. Unlike object detection, it provides the exact shape and extent of each scene element, enabling safe path planning.
- ✗
Optical character recognition (OCR)
Why it's wrong here
Optical character recognition (OCR) extracts text characters and words from an image, such as reading a traffic sign or license plate. It does not classify surfaces, objects, or scene geometry, and it ignores all non-text pixels. While useful for reading signs, OCR alone cannot provide the dense spatial layout required for autonomous navigation.
Go deeper
Related to this question
Learn chapter
Regression and Classification
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
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.
About these practice questions
Courseiva writes every AI-900 question from scratch — 985 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
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.