- A
Object detection and Optical Character Recognition
Object detection finds and locates vehicles/people, and OCR reads the text on license plates, fulfilling both requirements.
- B
Image analysis and face detection
Why wrong: Image analysis provides tags and descriptions but does not localize objects; face detection only detects faces, not vehicles or text.
- C
Semantic segmentation and image captioning
Why wrong: Semantic segmentation classifies every pixel but does not read text; image captioning generates a sentence describing the scene, not useful for license plate extraction.
- D
Spatial analysis and image classification
Why wrong: Spatial analysis focuses on movement and presence in a space, not object detection or text reading; image classification assigns a single label to the whole image.
Quick Answer
The answer is the combination of Object Detection and Optical Character Recognition (OCR). This is correct because the scenario requires two distinct Azure Computer Vision capabilities: object detection to locate and identify vehicles and people within live video frames, and OCR to extract the alphanumeric text from license plates. Object detection works by drawing bounding boxes around recognized objects, while OCR analyzes those regions to read embedded text, making the pair essential for automated license plate recognition. On the Microsoft Azure AI Fundamentals AI-900 exam, this question tests your understanding of how to match specific business needs to Azure’s pre-built vision services—a common trap is confusing image classification (which labels the whole image) with object detection (which locates multiple items). Remember the memory tip: “Detect the object, then read the text” to recall that object detection finds the vehicle, and OCR reads its plate.
AI-900 Practice Question: Describe features of computer vision workloads on Azure
This AI-900 practice question tests your understanding of describe features of computer vision workloads on azure. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A security company needs to analyze live video feeds from multiple cameras to detect specific objects (e.g., vehicles, people) and also read license plate numbers from vehicles. Which combination of Azure Computer Vision capabilities should they use?
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
Object detection and Optical Character Recognition
Option A is correct because the scenario requires two distinct capabilities: detecting specific objects (vehicles, people) in live video feeds, which is handled by Azure Computer Vision's Object Detection feature, and reading license plate numbers, which requires Optical Character Recognition (OCR). Object detection identifies and locates objects within an image or video frame, while OCR extracts text from images, making this combination ideal for the use case.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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 and Optical Character Recognition
Why this is correct
Object detection finds and locates vehicles/people, and OCR reads the text on license plates, fulfilling both requirements.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Image analysis and face detection
Why it's wrong here
Image analysis provides tags and descriptions but does not localize objects; face detection only detects faces, not vehicles or text.
- ✗
Semantic segmentation and image captioning
Why it's wrong here
Semantic segmentation classifies every pixel but does not read text; image captioning generates a sentence describing the scene, not useful for license plate extraction.
- ✗
Spatial analysis and image classification
Why it's wrong here
Spatial analysis focuses on movement and presence in a space, not object detection or text reading; image classification assigns a single label to the whole image.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse Image Analysis (which provides tags and descriptions) with Object Detection, or assume Face Detection can be generalized to other objects, leading them to choose Option B instead of the correct combination of Object Detection and OCR.
Detailed technical explanation
How to think about this question
Under the hood, Azure Computer Vision's Object Detection uses deep learning models like YOLO or Faster R-CNN to output bounding boxes and class labels for each detected object, while OCR leverages the Read API, which combines text detection and recognition using a multi-stage pipeline (e.g., CRAFT for detection and CRNN for recognition). In a real-world scenario, the video feed would be processed frame-by-frame, with object detection filtering frames containing vehicles, and then OCR applied to the region of interest (the license plate) to extract the alphanumeric string.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
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FAQ
Questions learners often ask
What does this AI-900 question test?
Describe features of computer vision workloads on Azure — This question tests Describe features of computer vision workloads on Azure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Object detection and Optical Character Recognition — Option A is correct because the scenario requires two distinct capabilities: detecting specific objects (vehicles, people) in live video feeds, which is handled by Azure Computer Vision's Object Detection feature, and reading license plate numbers, which requires Optical Character Recognition (OCR). Object detection identifies and locates objects within an image or video frame, while OCR extracts text from images, making this combination ideal for the use case.
What should I do if I get this AI-900 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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Last reviewed: Jun 11, 2026
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.
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