AI-900 Describe features of computer vision workloads on Azure Practice Test 9 — 15 Questions

Question 1

What is the Azure AI Custom Vision portal used for?

Accepted Answer

Training and evaluating custom image classification and object detection models without code. The Azure AI Custom Vision portal is a no-code web interface that allows users to upload images, label them, and train custom image classification or object detection models. It abstracts away the underlying machine learning code, making it accessible for non-developers to build and evaluate computer vision models tailored to their specific use cases.

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Managing Azure subscription billing for AI services

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Building chatbots using natural language understanding

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Monitoring the health of deployed AI services

Question 2

What information does Azure AI Face service provide about detected faces beyond just their location?

Accepted Answer

Age estimate, emotion, head pose, and other facial attributes. Azure AI Face service can extract a wide range of facial attributes beyond just the bounding box coordinates. These include age estimate, emotion (e.g., happiness, sadness, surprise), head pose (pitch, yaw, roll), facial hair, glasses, and more, making option B correct.

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Only the coordinates of the face bounding box

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The person's name and identity from a public database

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Only whether the face belongs to a human or not

Question 3

What does Azure AI Vision's image tagging feature return?

Accepted Answer

A list of descriptive keywords about the image content with confidence scores. Azure AI Vision's image tagging feature analyzes the content of an image and returns a list of descriptive keywords (tags) along with a confidence score for each tag. This allows applications to automatically identify objects, people, scenes, and actions within the image without requiring manual labeling.

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A JSON file with the image's color palette in hex codes

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GPS coordinates of where the photo was taken

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The camera settings used to capture the image

Question 4

What is the purpose of Azure AI Video Indexer's transcript feature?

Accepted Answer

To automatically convert speech in videos to searchable text with timestamps. Azure AI Video Indexer's transcript feature uses automatic speech recognition (ASR) to convert spoken audio in videos into a text transcript, which is then indexed with precise timestamps for each word or phrase. This enables users to search, navigate, and analyze video content by keyword or phrase, making the video's audio content fully searchable and accessible.

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To translate video subtitles into multiple languages

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To generate written scripts for producing new videos

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To extract text visible in video frames (on-screen text)

Question 5

What does Azure AI Vision's 'smart crops' feature do?

Accepted Answer

Identifies the most important region for optimal thumbnail cropping at any aspect ratio. Azure AI Vision's smart crops feature uses AI to identify the most important region of an image and then crops it to any specified aspect ratio while keeping that region in focus. This is particularly useful for generating thumbnails that maintain visual context across different display sizes, such as social media previews or responsive web design.

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Identifies agricultural crops in satellite imagery

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Removes unwanted background elements from images

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Detects when an image has been cropped or edited

Question 6

What is the purpose of training data labeling in computer vision model development?

Accepted Answer

Adding ground-truth annotations to training images so the model learns what to predict. Training data labeling is the process of adding ground-truth annotations (e.g., bounding boxes, segmentation masks, or class labels) to each training image. This supervised learning step provides the model with the correct answer for each example, enabling it to learn the mapping from image features to the desired output during training. Without labeled data, the model cannot be trained to recognize objects, classify scenes, or detect anomalies in computer vision tasks.

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Adding watermarks to images for copyright protection

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Compressing images to reduce storage costs during training

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Filtering out low-quality or blurry training images

Question 7

What is the Azure AI Face service's 'liveness detection' feature used for?

Accepted Answer

Determining whether a face is from a live person or a spoofing attempt (photo/video/mask). Azure AI Face's liveness detection is specifically designed to differentiate between a real, live human face and spoofing artifacts such as printed photos, video replays, or 3D masks. It analyzes subtle cues like micro-movements, texture, and depth to verify the presence of a living person, preventing unauthorized access in facial recognition systems.

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Detecting whether a person is alive based on their vital signs

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Counting how many people are in a live video stream

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Monitoring whether a person remains present during a video call

Question 8

What is the difference between Azure AI Vision and Azure AI Custom Vision?

Accepted Answer

Azure AI Vision offers pre-built models; Custom Vision trains custom models on your labeled images. Azure AI Vision provides pre-built, ready-to-use models for common computer vision tasks like image analysis, OCR, and facial recognition, requiring no training data. Azure AI Custom Vision, on the other hand, allows you to train custom models using your own labeled images to solve specific classification or object detection problems. This distinction makes option B correct because it accurately captures the core difference between a pre-built service and a customizable training platform.

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Azure AI Vision is faster; Custom Vision is more accurate

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Azure AI Vision analyzes only photos; Custom Vision analyzes documents

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They are different names for the same service

Question 9

Which Azure AI service is used to index and extract insights from large collections of videos at scale?

Accepted Answer

Azure AI Video Indexer. Azure AI Video Indexer is the correct service because it is specifically designed to ingest large collections of videos, extract metadata (such as transcripts, faces, emotions, and keyframes), and provide searchable insights at scale. Unlike other Azure AI services, Video Indexer combines multiple AI models (speech, vision, and language) into a single pipeline optimized for video content, making it the appropriate choice for indexing and extracting insights from video libraries.

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Azure AI Custom Vision

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Azure Blob Storage media services

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Azure AI Speech transcription only

Question 10

What is the Azure AI Vision Image Analysis 4.0's 'Florence' foundation model capable of?

Accepted Answer

Advanced image understanding including detailed captions, dense captioning, and multimodal embeddings. The Florence foundation model in Azure AI Vision Image Analysis 4.0 is a multimodal model designed for advanced image understanding. It can generate detailed image captions, produce dense captions (describing multiple regions within an image), and create multimodal embeddings that align visual and textual representations for tasks like image search and similarity.

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Only detecting faces in images

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Only processing medical imaging for diagnostic purposes

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Converting images into 3D models

Question 11

What is the purpose of the Azure AI Document Intelligence's prebuilt models?

Accepted Answer

Extracting structured data from common document types (invoices, receipts, IDs) without custom training. Azure AI Document Intelligence's prebuilt models are designed to extract structured data from common document types such as invoices, receipts, and IDs without requiring any custom training. They leverage pre-trained neural networks that recognize fields like invoice totals, receipt line items, and ID numbers, enabling rapid data extraction for standard forms. This aligns with the purpose of reducing manual data entry and accelerating document processing workflows.

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Training custom document extraction models for unique business forms

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Translating documents from one language to another

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Converting documents to PDF format for archiving

Question 12

What is the primary use case for Azure AI Document Intelligence's layout model?

Accepted Answer

Extracting the structural layout of documents including tables, text blocks, and positions. Azure AI Document Intelligence's layout model is designed to extract the structural layout of documents, including tables, text blocks, and their spatial positions. This enables downstream processing like OCR, form understanding, and document analysis by preserving the original reading order and layout hierarchy.

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Generating visual layouts for new document templates

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Converting documents between different file formats (PDF to DOCX)

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Checking documents for grammatical and spelling errors

Question 13

What does the 'read' operation in Azure AI Vision do?

Accepted Answer

Extracts printed and handwritten text from images and documents. The 'read' operation in Azure AI Vision is specifically designed to extract printed and handwritten text from images and documents using Optical Character Recognition (OCR) technology. It returns the detected text along with bounding box coordinates and confidence scores, making it suitable for digitizing documents, processing forms, and extracting text from photos.

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Reads and describes what's happening in a video

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Reads and verifies digital signatures in documents

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Reads metadata (EXIF data) embedded in image files

Question 14

What is the difference between face detection and face identification?

Accepted Answer

Face detection finds face locations; face identification determines who the person is from an enrolled database. Face detection is a computer vision task that locates human faces in an image or video, returning bounding box coordinates. Face identification (or recognition) goes a step further by matching a detected face against a database of enrolled individuals to determine a specific identity. Option B correctly distinguishes these two operations: detection finds where faces are, while identification determines who the person is.

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Face detection identifies who the person is; face identification counts how many faces are present

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Face detection works on videos; face identification works on static images only

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They are the same operation with different names

Question 15

What is the Azure AI Custom Vision service's 'compact' domain used for?

Accepted Answer

Producing exportable models optimized for deployment on edge devices with limited compute. The Azure AI Custom Vision service's 'compact' domain is specifically designed to produce models that can be exported to formats like TensorFlow, ONNX, or CoreML for deployment on edge devices with limited compute, memory, and power. This domain trades some accuracy for a smaller model footprint, enabling real-time inference on devices such as cameras, drones, or IoT gateways.

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Training models on a compact (small) dataset with fewer than 50 images

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Creating more compact API responses with less metadata

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Training models that use less storage in Azure blob containers