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 warehouse deploys cameras to automatically process incoming packages. The system must read the serial numbers printed on each package label to update inventory records. The labels often have varied fonts and sizes, and may be slightly rotated. Which Azure Computer Vision capability should be used to extract the serial numbers?
Why wrong: Object detection finds bounding boxes around objects (e.g., boxes, people) but does not read text. It cannot extract serial numbers.
B
Optical Character Recognition (OCR)
OCR extracts text from images, handling various fonts, sizes, and orientations. This is the standard Azure Computer Vision capability for reading printed text like serial numbers.
C
Image classification
Why wrong: Image classification assigns a label to the entire image (e.g., 'box') but cannot read or extract specific text characters.
D
Facial recognition
Why wrong: Facial recognition identifies human faces. It is irrelevant for reading text on labels.
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 Azure Computer Vision capability because it is specifically designed to extract printed or handwritten text from images, including serial numbers with varied fonts, sizes, and rotations. Azure's OCR API (part of Computer Vision) can handle skewed or rotated text by automatically detecting and correcting orientation before recognizing characters, making it ideal for warehouse labels that are not perfectly aligned.
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 finds bounding boxes around objects (e.g., boxes, people) but does not read text. It cannot extract serial numbers.
✓
Optical Character Recognition (OCR)
Why this is correct
OCR extracts text from images, handling various fonts, sizes, and orientations. This is the standard Azure Computer Vision capability for reading printed text like serial numbers.
Related concept
Read the scenario before looking for a memorised answer.
✗
Image classification
Why it's wrong here
Image classification assigns a label to the entire image (e.g., 'box') but cannot read or extract specific text characters.
✗
Facial recognition
Why it's wrong here
Facial recognition identifies human faces. It is irrelevant for reading text on labels.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse object detection (which can 'see' labels) with OCR, not realizing that object detection only locates objects without reading any text content on them.
Detailed technical explanation
How to think about this question
Azure's OCR engine uses a deep-learning-based model that first detects text regions via a text detection algorithm (e.g., CRAFT or similar), then performs character recognition using a sequence-to-sequence model. It can handle text at any angle (up to 360 degrees) and automatically corrects perspective distortions, which is critical for real-world warehouse scenarios where labels may be tilted or partially obscured. The API returns both the raw text and bounding box coordinates, enabling downstream inventory systems to update records with high accuracy.
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 Azure Computer Vision capability because it is specifically designed to extract printed or handwritten text from images, including serial numbers with varied fonts, sizes, and rotations. Azure's OCR API (part of Computer Vision) can handle skewed or rotated text by automatically detecting and correcting orientation before recognizing characters, making it ideal for warehouse labels that are not perfectly aligned.
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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