AI-102 Implement computer vision solutions Practice Test 5 — 15 Questions

Question 1

You need to analyze videos stored in Azure Blob Storage to detect objects and generate timestamps. Which Azure service should you use?

Accepted Answer

Azure Video Indexer. Azure Video Indexer (D) is the correct choice because it is specifically designed to analyze videos, extracting insights such as object detection, scene segmentation, and timestamps. It uses AI models to process video content stored in Azure Blob Storage and generates a timeline of detected objects, making it ideal for this scenario.

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Azure Custom Vision

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Azure Form Recognizer

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Azure Computer Vision

Question 2

A company uses Azure Computer Vision to moderate user-generated content. The solution must detect adult content and flag it. Which API should you call?

Accepted Answer

Analyze API with visualFeatures set to 'Adult'. The Analyze API with the visualFeatures parameter set to 'Adult' is the correct choice because Azure Computer Vision's Analyze Image operation includes an 'Adult' category that specifically detects adult, racy, and gory content in images. This API returns a confidence score (0 to 1) for each category, allowing the solution to flag content based on a threshold. The other APIs do not provide adult content moderation capabilities.

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Read API

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Detect API

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Describe API

Question 3

You are deploying a Custom Vision model to a production environment. The model must handle 100 predictions per second with low latency. Which deployment option should you choose?

Accepted Answer

Use a paid tier prediction endpoint with sufficient capacity.. A paid tier prediction endpoint in Azure Custom Vision is designed to handle production-scale workloads with dedicated compute resources, supporting up to 100 predictions per second with low latency. The Free tier is rate-limited and cannot sustain this throughput, while exporting as a Docker container introduces additional overhead and scaling complexity that may not guarantee the required latency or throughput without manual orchestration.

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Use the Free tier prediction endpoint.

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Export the model as a Docker container and run it on Azure Container Instances.

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Use the Training API to make predictions.

Question 4

You need to extract handwritten text from scanned forms. Which Azure Computer Vision feature should you use?

Accepted Answer

Read API. The Read API is specifically designed for extracting printed and handwritten text from images and documents, including scanned forms. It uses advanced deep-learning models optimized for text recognition and is the correct service for this task in Azure Computer Vision.

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OCR API (optical character recognition)

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Tag API

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Describe API

Question 5

Which TWO Azure services can be used to perform optical character recognition (OCR) on images?

Accepted Answer

Azure Computer Vision Read API. Azure Computer Vision Read API is correct because it provides a dedicated OCR capability that extracts printed and handwritten text from images and documents. It uses deep learning models to detect text regions, recognize characters, and return structured output with bounding boxes and confidence scores.

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Azure Face API

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Azure Video Indexer

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Azure Custom Vision

Question 6

Which THREE factors should you consider when selecting a pricing tier for Azure Computer Vision in a production environment?

Accepted Answer

Data residency requirements. Data residency requirements (Option C) are critical when selecting a pricing tier for Azure Computer Vision because the service processes images in specific regional data centers, and some tiers (e.g., Standard S0) support multi-region processing while others may be restricted. Compliance with regulations like GDPR or HIPAA may require that image data never leaves a particular geography, directly influencing which tier and region you can choose.

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Availability of free tier

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Type of storage account for images

Question 7

Which TWO features are available in Azure Computer Vision but not in Azure Custom Vision?

Accepted Answer

Optical character recognition (OCR). Optical Character Recognition (OCR) and image captioning are features available in Azure Computer Vision but not in Azure Custom Vision. Azure Computer Vision provides pre-built OCR APIs (e.g., Read API) for extracting text from images and captioning models for generating human-readable descriptions. Azure Custom Vision is focused on training custom models for image classification and object detection, and does not include built-in OCR or captioning capabilities.

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Object detection

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Image classification

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Face detection

Question 8

You call the Azure Computer Vision Analyze API with the above request body. The response includes a 'description' object with captions. Which parameter is responsible for generating captions?

Accepted Answer

Description. The 'description' parameter in the Azure Computer Vision Analyze API request body explicitly requests the service to generate human-readable captions and tags that describe the image content. When set to true, it enables the 'description' object in the response, which contains an array of captions with confidence scores. This is the direct mechanism for caption generation.

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Tags

Question 9

You are creating a new Custom Vision project with the above JSON. The domainId corresponds to the 'Logo' domain. Which type of model will this project train?

Accepted Answer

A multilabel image classification model for logo detection. The 'Logo' domain in Custom Vision is specifically designed for image classification tasks, not object detection. When you create a project with the 'Logo' domain, it trains a multilabel image classification model, meaning each image can be assigned multiple labels (e.g., multiple logos in one image). This domain is optimized for identifying and classifying logos within images, making it distinct from object detection or general classification.

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An object detection model for logo detection

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An optical character recognition model

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A general image classification model

Question 10

You are deploying a Custom Vision model to an Azure Machine Learning managed endpoint using the above ARM template snippet. The deployment fails with a timeout error. Which parameter should you adjust?

Accepted Answer

Increase scoringTimeout to 'PT10M'. The deployment fails due to a timeout error, which indicates that the model inference is taking longer than the default scoring timeout (typically 5 seconds for Azure ML managed endpoints). Increasing the scoringTimeout parameter to 'PT10M' (ISO 8601 duration for 10 minutes) extends the allowed time for the endpoint to respond, resolving the timeout without altering compute or authentication settings.

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Change model version to 2

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Change compute to 'GPU'

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Change authMode to 'AAD'

Question 11

A company uses Azure AI Vision to analyze product images in an e-commerce application. The solution uses the Analyze Image API with OCR. Recently, the OCR accuracy has decreased for images with handwritten text on product labels. What should the team do to improve accuracy?

Accepted Answer

Switch to the Read API for OCR.. The Read API (part of Azure AI Vision) is specifically optimized for extracting text from images, including handwritten text, and provides higher accuracy for OCR scenarios compared to the general-purpose Analyze Image API. The Analyze Image API's OCR feature is designed for printed text and does not handle handwritten content as effectively. By switching to the Read API, the team leverages a dedicated OCR engine that supports both printed and handwritten text, improving accuracy for product labels.

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Enable OCR in the Analyze Image API configuration.

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Use Azure AI Document Intelligence prebuilt receipt model.

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Retrain the OCR model with additional handwritten samples.

Question 12

You are designing a computer vision solution for a retail chain to detect shelf stockouts using store camera feeds. Videos are processed in near real-time. Which combination of Azure services should you use to minimize latency and cost?

Accepted Answer

Use Azure Video Analyzer for Media (formerly Video Indexer) with an Azure IoT Edge module processing video at the edge.. Azure Video Analyzer for Media (formerly Video Indexer) with an Azure IoT Edge module enables near real-time processing at the edge, minimizing latency by avoiding round trips to the cloud, and reduces cost by processing video locally and only sending relevant insights (e.g., stockout detections) to the cloud. This architecture is ideal for retail chains with store camera feeds that require low-latency, cost-effective stockout detection.

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Use Azure Video Indexer to analyze videos and send results to Azure SQL Database.

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Use Azure Custom Vision to detect stockouts in video frames.

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Use Azure Media Services to transcode video and then run Custom Vision on key frames.

Question 13

You are building a solution to detect if a person is wearing a hard hat in construction site images. You have a small dataset of labeled images. Which Azure service should you use?

Accepted Answer

Azure Custom Vision. Azure Custom Vision is the correct choice because it allows you to train a custom image classification model with your own small dataset of labeled construction site images to detect whether a person is wearing a hard hat. Unlike pre-built services, Custom Vision specializes in fine-tuning models for specific visual concepts that are not covered by general-purpose APIs, making it ideal for niche object detection tasks like hard hat detection.

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Azure AI Vision Image Analysis

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Azure Video Indexer

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Azure AI Document Intelligence

Question 14

A healthcare organization uses Azure AI Vision to analyze medical images for abnormalities. The solution must ensure patient data is not stored in the service's logs. What should you configure?

Accepted Answer

Set the logging level to 'None' in the Azure AI Vision resource diagnostic settings.. Setting the logging level to 'None' in the Azure AI Vision resource diagnostic settings prevents any patient data from being captured in the service's logs. This directly addresses the requirement to ensure patient data is not stored, as diagnostic logs can contain request and response payloads that include medical image metadata or analysis results.

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Use a private endpoint to access the Azure AI Vision resource.

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Enable Customer-Managed Keys (CMK) for the resource.

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Use Azure Policy to restrict data residency.

Question 15

You are designing a solution that uses Azure AI Vision to extract text from scanned invoices. The invoices vary in layout and include both printed and handwritten fields. The solution must achieve high accuracy with minimal manual labeling. Which approach should you recommend?

Accepted Answer

Use the Azure AI Document Intelligence prebuilt invoice model.. The Azure AI Document Intelligence prebuilt invoice model is specifically designed to extract common fields from invoices with high accuracy, handling both printed and handwritten text without requiring manual labeling. It uses advanced OCR and deep learning models trained on thousands of invoices, making it ideal for varied layouts and minimal setup.

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Use the Read API to extract all text and then use a custom regex to parse fields.

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Train a Custom Vision object detection model to locate fields.

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Label hundreds of invoices and train a custom Azure AI Document Intelligence model.