- A
Image Classification and OCR
Why wrong: Image classifies the entire scene but does not provide location of objects; OCR reads text. This combination lacks object detection for pedestrians.
- B
Semantic Segmentation and OCR
Why wrong: Semantic segmentation provides pixel-level classification but is not designed to read text; OCR covers text reading, but pedestrian detection is better handled by object detection for bounding boxes.
- C
Optical Character Recognition (OCR) and Object Detection
OCR reads text from signs, and object detection finds and locates pedestrians, which together meet both requirements.
- D
Face Detection and OCR
Why wrong: Face detection finds only faces, not all pedestrians, and cannot detect a pedestrian from behind or without a visible face.
Quick Answer
The correct combination is Optical Character Recognition (OCR) and Object Detection. OCR is needed to read the speed limit text from traffic signs by extracting printed characters from images, while Object Detection identifies pedestrians and provides bounding boxes around their locations, enabling the vehicle to track their presence and movement. On the Microsoft Azure AI Fundamentals AI-900 exam, this scenario tests your understanding of how different Azure Computer Vision capabilities serve distinct real-world tasks—reading text versus detecting objects. A common trap is confusing Object Detection with Image Classification, which only labels the entire image rather than locating multiple objects. To remember, think of a self-driving car: it must both “read the sign” (OCR) and “spot the person” (Object Detection).
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.
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?
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.
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.
- ✗
Image Classification and OCR
Why it's wrong here
Image classifies the entire scene but does not provide location of objects; OCR reads text. This combination lacks object detection for pedestrians.
- ✗
Semantic Segmentation and OCR
Why it's wrong here
Semantic segmentation provides pixel-level classification but is not designed to read text; OCR covers text reading, but pedestrian detection is better handled by object detection for bounding boxes.
- ✓
Optical Character Recognition (OCR) and Object Detection
Why this is correct
OCR reads text from signs, and object detection finds and locates pedestrians, which together meet both requirements.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Face Detection and OCR
Why it's wrong here
Face detection finds only faces, not all pedestrians, and cannot detect a pedestrian from behind or without a visible face.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates 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.
Detailed technical explanation
How to think about this question
Under the hood, Azure Computer Vision's OCR uses a deep-learning-based text recognition model (e.g., CRNN with CTC loss) to extract text from images, while Object Detection uses models like YOLO or Faster R-CNN to output bounding boxes and class labels for each detected object. In a real-world scenario, the vehicle's system would run OCR on traffic sign regions to read speed limits and Object Detection on the full camera frame to localize pedestrians, enabling simultaneous text reading and spatial awareness.
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: 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.
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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