AI-900 Describe features of computer vision workloads on Azure — Timed 50 Questions

Question 1

A museum wants to create an application that automatically generates descriptive captions for uploaded photos of artworks. The captions should describe the main subject, scene, and artistic style. Which Azure Computer Vision capability should they use?

50 minute time limit — choose an answer to begin.

Accepted Answer

Image Analysis (with description feature). The Image Analysis capability in Azure Computer Vision includes a description feature that generates human-readable captions summarizing the main subject and scene of an image, using pre-trained deep learning models. It does not explicitly identify artistic style, but it is the only option that provides automated image captioning without custom training.

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Optical Character Recognition (OCR)

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Face API

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Custom Vision (object detection)

Question 2

A social media platform wants to automatically generate a textual description for each user-uploaded image to assist visually impaired users. Which prebuilt Azure Computer Vision feature should they use?

Accepted Answer

Image captioning. Azure AI Vision's Image Analysis API provides an image captioning feature that generates a human-readable sentence describing the entire image. Object detection returns object labels and bounding boxes, OCR extracts text, and face detection identifies faces, none of which produce a full textual description.

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Object detection

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Optical Character Recognition (OCR)

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Face detection

Question 3

A retail company wants to use Azure AI services to monitor shelf inventory. They need to detect whether specific products (e.g., 'Brand A cereal', 'Brand B cereal') are present on a shelf and count the number of units of each product. They have a labeled dataset with images of each product category. Which Azure AI capability should they use?

Accepted Answer

Custom object detection. The correct capability is custom object detection, which is offered by Azure Custom Vision. You cannot train Azure Computer Vision to detect specific product categories; prebuilt Computer Vision only supports common objects and generic image analysis. To detect and count specific products like 'Brand A cereal', you need to train a custom model using Azure Custom Vision. Therefore, the intended answer (A) is correct in spirit, but it must be clarified that this is a Custom Vision capability, not a Computer Vision capability.

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Optical character recognition (OCR)

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Prebuilt image analysis (Describe Image)

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Facial recognition

Question 4

A manufacturing company wants to use Azure Computer Vision to automatically inspect products on an assembly line for defects. They need to identify and locate specific types of defects (e.g., scratch, dent, crack) in product images. Which Azure Computer Vision capabilities could be used together to achieve this? (Select two options.)

Accepted Answer

Object Detection. Object Detection in Azure Computer Vision can identify and locate multiple specific defect types (e.g., scratch, dent, crack) within product images by drawing bounding boxes around each defect. Semantic Segmentation goes further by classifying each pixel, enabling precise localization and classification of defects at the pixel level. Together, these capabilities allow the system to both identify defect types and pinpoint their exact locations on the assembly line.

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Optical Character Recognition (OCR)

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Image Classification

Question 5

A security company needs to identify individuals in a crowd by matching their faces against a database of known persons of interest. The system must detect faces, verify the identities, and provide a confidence score. Which Azure AI capability should they use?

Accepted Answer

Facial recognition. Azure Face service (Face API) is the correct capability for detecting faces, verifying identities against a database, and returning confidence scores. Azure Computer Vision only performs face detection (locating faces), not identity matching.

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Optical character recognition (OCR)

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Image classification

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Object detection

Question 6

A company needs to extract text from scanned invoices and receipts. Which Azure services are suitable for this task? (Choose two.)

Accepted Answer

Computer Vision. Computer Vision (option A) is correct because it provides OCR capabilities to extract printed and handwritten text from images, including scanned invoices and receipts. Its Read API can process text from various surfaces and layouts. Azure AI Document Intelligence (option B) is also correct because it is specifically designed to extract text, key-value pairs, and tables from documents such as invoices and receipts, offering prebuilt models for these document types. Both services are suitable for this task.

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Azure AI Language

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Custom Vision

Question 7

Which model powers Azure AI Vision's image analysis capabilities such as dense captioning, grounded detection, and image-text search?

Accepted Answer

Florence-powered vision-language capabilities for dense captioning, grounded detection, and image-text search. Azure AI Vision's image analysis features (dense captioning, grounded detection, and image-text search) are powered by the Florence model. Image generation is a separate capability provided by DALL-E via Azure OpenAI Service, not by Azure AI Vision's Florence model.

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Creating image files from raw binary data uploaded to Azure Blob Storage

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Generating high-resolution versions of low-resolution input images

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Automatically generating training image variations through data augmentation

Question 8

What is 'medical imaging AI' and what Azure services support clinical imaging applications?

Accepted Answer

AI that analyses radiology and pathology images for clinical decision support — assisted by Azure AI Health Insights. Medical imaging AI refers to AI systems that analyze radiology and pathology images to assist clinicians with diagnosis, treatment planning, and clinical decision support. Azure supports such applications with services like Azure AI Vision for image analysis and Azure Machine Learning for custom models. Azure AI Health Insights can complement clinical workflows by analyzing health data and reports, but it does not directly detect abnormalities in X-rays, CT scans, or MRIs.

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AI systems that replace radiologists by autonomously making all diagnoses from scans

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Storing and managing medical images in Azure Blob Storage with HIPAA compliance

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Automatically scheduling patient appointments based on medical image analysis results

Question 9

Match each Azure AI service tier to its description.

Accepted Answer

Free: Suitable for evaluation and small-scale testing with limited transactions per month.. Azure AI services primarily offer Free (F0) and Standard (S0) tiers. There is no standard 'Enterprise' tier across Azure AI services. The description for Enterprise does not correspond to a real tier, so E should not be considered correct.

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Free: Designed for production workloads with SLA.

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Standard: Limited to evaluation and testing, not for production.

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Enterprise: High throughput, advanced features, dedicated capacity.

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Enterprise: Free tier with limited transactions.

Question 10

A transportation company wants to automatically identify whether an image contains a car, a truck, or a motorcycle. The system should output a single label for the entire image. Which computer vision capability in Azure should they use?

Accepted Answer

Image classification. Image classification assigns a single label to an entire image based on its dominant content. Since the requirement is to output one label (car, truck, or motorcycle) per image, this maps directly to Azure's Custom Vision image classification capability, which trains a model to categorize whole images into predefined classes.

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Object detection

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Optical Character Recognition (OCR)

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Semantic segmentation

Question 11

A manufacturing company wants to use Azure AI to detect surface defects on metal parts. The team has a small set of labeled images of defective and non-defective parts, and images will be taken under various lighting conditions and angles. They need a solution that can leverage a pre-trained model and adapt it to their specific defect types with minimal new training data. Which approach should they take?

Accepted Answer

A. Use Custom Vision to train a classification or object detection model with transfer learning. Custom Vision allows you to use transfer learning, which starts from a pre-trained model and fine-tunes it on your small labeled dataset of defective and non-defective parts. This approach is ideal when you have limited training data and need to adapt the model to specific defect types under varying lighting and angles, as Custom Vision supports both classification and object detection for surface defects.

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B. Use the Optical Character Recognition (OCR) API

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C. Use the Describe Image API (Image Captioning)

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D. Use the Face API

Question 12

A logistics company receives thousands of handwritten shipping labels each day. They want to use Azure AI to automatically read the handwritten addresses and convert them into digital text. Which Azure Cognitive Services capability should they use?

Accepted Answer

Optical character recognition (OCR). Optical character recognition (OCR) is the correct Azure Cognitive Services capability because it is specifically designed to extract printed or handwritten text from images and convert it into machine-readable digital text. In this scenario, the logistics company needs to read handwritten addresses from shipping labels, which is a classic OCR workload. Azure's Computer Vision OCR API (including the Read API) can handle both printed and handwritten text, making it the ideal choice for this task.

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Image classification

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Object detection

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Face detection

Question 13

A logistics warehouse uses a conveyor belt system to move packages. They need to automatically read the alphanumeric serial numbers printed on labels attached to each box. The labels may have different fonts and be somewhat dusty. Which Azure Computer Vision feature should they use?

Accepted Answer

Optical Character Recognition (OCR) using the Read API. The Read API, part of Azure Computer Vision's OCR capabilities, is specifically designed to extract printed and handwritten text from images, including alphanumeric serial numbers. It can handle varying fonts and degraded image quality (e.g., dusty labels) by using deep-learning models optimized for text recognition. This makes it the correct choice for reading serial numbers from conveyor belt packages.

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Image Classification

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Object Detection

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Image Analysis (captioning and tagging)

Question 14

A retail company wants to build a system that can verify the identity of customers by comparing their live photo with an uploaded government-issued ID photo. Which Azure Computer Vision service should they use to perform the face comparison?

Accepted Answer

Azure Face API. The Azure Face API is specifically designed for face detection, verification, and comparison tasks. It can compare a live photo against a reference photo (such as a government-issued ID) using its 'Verify' operation, which returns a confidence score indicating whether the two faces belong to the same person. This makes it the correct choice for identity verification scenarios.

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Azure Computer Vision - Image Analysis

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Azure Custom Vision

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Azure Form Recognizer

Question 15

A retail warehouse uses a camera system to locate and count boxes on shelves. The system needs to output the exact positions of each box by drawing a rectangular frame around it in the image. Which Azure Computer Vision capability should they use?

Accepted Answer

Object detection. Object detection is the correct capability because it identifies and localizes multiple objects within an image by drawing bounding boxes around each detected instance. In this scenario, the system needs to locate and count individual boxes on shelves, which requires both classification (what is a box) and localization (where each box is), exactly what object detection provides.

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Image classification

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Semantic segmentation

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Optical Character Recognition (OCR)

Question 16

A construction company uses drone images to survey construction sites. They need an automated system that can identify specific types of heavy equipment (e.g., bulldozers, cranes, excavators) in an image and also draw precise pixel-level outlines around each equipment type. Which Azure Computer Vision capability should they use?

Accepted Answer

Semantic segmentation. Semantic segmentation is the correct capability because it assigns a class label (e.g., bulldozer, crane, excavator) to every pixel in the image, producing precise pixel-level outlines around each equipment type. Object detection only provides bounding boxes, not pixel-level masks, while image classification labels the entire image without localization. OCR is irrelevant as it extracts text, not equipment shapes.

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Object detection

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Image classification

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Optical Character Recognition (OCR)

Question 17

A brand monitoring company wants to automatically detect the presence of specific logos (e.g., Apple, Coca-Cola) in social media images. The logos can appear in various orientations and sizes within the image. Which Azure Computer Vision capability is specifically designed to identify popular brands from their logos?

Accepted Answer

Brand Detection. Brand Detection is a specialized Azure Computer Vision capability that uses a pre-trained model to identify thousands of global brands from their logos in images. It is specifically designed to handle variations in logo orientation, size, and placement, making it the correct choice for this scenario.

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Image Classification

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Object Detection

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Optical Character Recognition

Question 18

A real estate company wants to create an application that automatically generates floor plans from photographs of rooms. The application needs to identify and delineate every pixel in the image that corresponds to walls, doors, windows, and furniture. Which Azure Computer Vision capability should the company use?

Accepted Answer

Semantic Segmentation. Semantic segmentation is the correct choice because it classifies every pixel in an image into predefined categories (e.g., walls, doors, windows, furniture), producing a pixel-level mask. This is exactly what the application needs to delineate each structural element and object in the room photograph, enabling accurate floor plan generation.

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Object Detection

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Image Classification

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Optical Character Recognition (OCR)

Question 19

A company needs to automatically extract text from scanned invoices that contain both printed text and handwritten notes. Which Azure AI service is specifically designed to handle this type of document?

Accepted Answer

Azure AI Document Intelligence (formerly Form Recognizer). Azure AI Document Intelligence (formerly Form Recognizer) is specifically designed to extract text, key-value pairs, and tables from scanned documents, including invoices with both printed text and handwritten notes. It uses optical character recognition (OCR) combined with deep learning models to handle mixed content, making it the correct choice for this scenario.

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Azure Face API

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Azure Custom Vision

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Azure Video Indexer

Question 20

A quality control manager at a bottling plant needs an automated system to inspect images of bottles coming off the production line. The system must determine whether each bottle has a correctly sealed cap or is defective (cap missing or crooked). The manager has a set of labeled images showing both acceptable and defective bottles. Which Azure Computer Vision service should they use to build a model that classifies each bottle image as 'acceptable' or 'defective'?

Accepted Answer

Azure Custom Vision (Image Classification). Azure Custom Vision (Image Classification) is the correct service because it allows you to upload labeled images of bottles (acceptable and defective) and train a custom image classification model to distinguish between the two classes. This service is specifically designed for scenarios where you need to classify images into user-defined categories without requiring deep learning expertise.

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Azure Face API

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Azure Form Recognizer

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Azure OCR (Read API)