The correct choice is Optical Character Recognition (OCR) because it is purpose-built to extract printed or handwritten text from images, handling variations in fonts, sizes, orientations, and even partially obscured text. Azure Computer Vision’s OCR capability, specifically the Read API, uses deep-learning models to digitize text from natural scenes, making it ideal for reading package labels in a logistics environment. On the AI-900 exam, this scenario tests your understanding of when to use OCR versus other Computer Vision features like object detection or image analysis, which do not extract text. A common trap is confusing OCR with the Form Recognizer service, but remember that OCR is for raw text extraction from any image, while Form Recognizer is for structured data from forms. Memory tip: OCR = Optical Character Reader, and if you need to “read” text from a picture, think “OCR reads the letters.”
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 logistics company uses overhead cameras at a shipping dock to read labels on packages. The labels contain text in various fonts, sizes, and orientations, and sometimes the text is partially obscured. Which Azure Computer Vision capability should they use to extract the text from these labels?
Why wrong: Object detection identifies and locates objects (e.g., boxes, vehicles) within an image, but does not read text.
B
Optical Character Recognition (OCR)
OCR extracts text from images and is ideal for reading labels with varying fonts, sizes, and orientations.
C
Image classification
Why wrong: Image classification assigns a single label to an entire image (e.g., 'shipping dock'), but cannot read specific text content.
D
Semantic segmentation
Why wrong: Semantic segmentation divides an image into pixel-level regions belonging to different classes (e.g., background, label), but does not extract text characters.
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 correct choice because it is specifically designed to extract printed or handwritten text from images, handling variations in fonts, sizes, orientations, and partial occlusion. Azure Computer Vision's OCR API (Read API) uses deep-learning models to detect and digitize text from natural scenes, making it ideal for reading labels on packages in a logistics environment.
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
Why it's wrong here
Object detection identifies and locates objects (e.g., boxes, vehicles) within an image, but does not read text.
✓
Optical Character Recognition (OCR)
Why this is correct
OCR extracts text from images and is ideal for reading labels with varying fonts, sizes, and orientations.
Related concept
Read the scenario before looking for a memorised answer.
✗
Image classification
Why it's wrong here
Image classification assigns a single label to an entire image (e.g., 'shipping dock'), but cannot read specific text content.
✗
Semantic segmentation
Why it's wrong here
Semantic segmentation divides an image into pixel-level regions belonging to different classes (e.g., background, label), but does not extract text characters.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse object detection (which finds objects) with OCR (which reads text), or assume image classification can handle text extraction, when in fact OCR is the only Azure Computer Vision capability purpose-built for digitizing text from images.
Detailed technical explanation
How to think about this question
Azure's OCR capability is powered by the Read API, which uses a combination of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) with attention mechanisms to recognize text at the word and line level, even in challenging conditions like skewed angles or partial occlusion. The API returns bounding boxes and confidence scores for each text element, enabling downstream processing such as sorting or routing packages. In real-world logistics, OCR can handle mixed fonts and low-contrast labels, but it may struggle with severe blur or extremely small text, requiring pre-processing like image enhancement.
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 correct choice because it is specifically designed to extract printed or handwritten text from images, handling variations in fonts, sizes, orientations, and partial occlusion. Azure Computer Vision's OCR API (Read API) uses deep-learning models to detect and digitize text from natural scenes, making it ideal for reading labels on packages in a logistics environment.
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
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. 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?
easy
✓ A.Optical Character Recognition (OCR)
B.Object detection
C.Image classification
D.Facial recognition
Why A: 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.
Variation 2. A parking lot management company uses security cameras to monitor vehicles. They need to both detect the presence of license plates in an image and read the alphanumeric characters on those plates. Which Azure Computer Vision capability should they use to achieve both requirements?
hard
A.Image Analysis (describe image and detect objects)
✓ B.Optical Character Recognition (OCR) - Read API
C.Face API
D.Custom Vision (object detection)
Why B: Option B (OCR - Read API) is correct because Azure's Read API is specifically designed to both detect the presence of text (including license plates) in an image and extract the alphanumeric characters from that text. This meets both requirements—detecting the plate and reading its characters—in a single call, using deep-learning-based recognition models optimized for printed and handwritten text.
Last reviewed: Jun 11, 2026
Question Discussion
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