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 parking management company uses cameras at the entrance and exit of a lot. They need to automatically read the license plate numbers of each car as it enters and exits. Which Azure Computer Vision capability is specifically designed for this task?
OCR extracts text from images, including license plate numbers, without needing custom training.
B
Object detection
Why wrong: Object detection identifies and locates objects in an image (e.g., cars), but it does not read text or characters.
C
Image classification
Why wrong: Image classification assigns a label to the entire image (e.g., 'car entering'), but it cannot extract specific text like license plate numbers.
D
Facial recognition
Why wrong: Facial recognition identifies or verifies people by their faces. It is not designed for reading text on license plates.
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)
Optical Character Recognition (OCR) is the Azure Computer Vision capability specifically designed to extract printed or handwritten text from images, including license plate numbers. In this scenario, the cameras capture images of cars entering and exiting, and OCR processes those images to read the alphanumeric characters on the license plates. This is the exact use case for OCR, as it can handle varied fonts, angles, and lighting conditions common in parking lot environments.
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.
✓
Optical Character Recognition (OCR)
Why this is correct
OCR extracts text from images, including license plate numbers, without needing custom training.
Related concept
Read the scenario before looking for a memorised answer.
✗
Object detection
Why it's wrong here
Object detection identifies and locates objects in an image (e.g., cars), but it does not read text or characters.
✗
Image classification
Why it's wrong here
Image classification assigns a label to the entire image (e.g., 'car entering'), but it cannot extract specific text like license plate numbers.
✗
Facial recognition
Why it's wrong here
Facial recognition identifies or verifies people by their faces. It is not designed for reading text on license plates.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse object detection with OCR, thinking that detecting a license plate as an object is sufficient, but OCR is required to actually read the alphanumeric text on the plate.
Detailed technical explanation
How to think about this question
Azure Computer Vision's OCR API uses deep learning models based on convolutional neural networks (CNNs) combined with recurrent neural networks (RNNs) and Connectionist Temporal Classification (CTC) for sequence recognition. It can handle rotated, skewed, or partially obscured text by first detecting text regions via a text detection model, then recognizing characters in those regions. In real-world parking systems, OCR must be robust to varying lighting, dirt on plates, and different plate formats (e.g., US vs. EU), which Azure's OCR handles through pre-trained models that support over 100 languages.
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.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this AI-900 question in full detail.
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) — Optical Character Recognition (OCR) is the Azure Computer Vision capability specifically designed to extract printed or handwritten text from images, including license plate numbers. In this scenario, the cameras capture images of cars entering and exiting, and OCR processes those images to read the alphanumeric characters on the license plates. This is the exact use case for OCR, as it can handle varied fonts, angles, and lighting conditions common in parking lot environments.
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.
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Question Discussion
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