AI-102 Implement computer vision solutions Practice Test 2 — 15 Questions

Question 1

You are calling the Azure AI Vision Image Analysis API with the above JSON request. The API returns a response that includes tags, objects, and a caption. However, the caption does not describe the image accurately. Which change should you make to improve the caption quality?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Set gender-neutral-caption to false. Setting `gender-neutral-caption` to false forces the Azure AI Vision Image Analysis API to generate captions that include gender-specific terms (e.g., 'man' or 'woman') instead of neutral terms like 'person'. This improves caption accuracy when the image clearly depicts a person whose gender is identifiable, because the default (true) may produce a vague description that fails to capture the subject's characteristics.

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Change model-version to a newer version

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Add 'read' feature to the request

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Set language to 'en-US'

Question 2

You deploy the above ARM template to create an Azure AI Vision resource. After deployment, you try to call the Vision API from your application but receive a 403 Forbidden error. What is the most likely cause?

Accepted Answer

The network ACLs block all traffic by default. When you deploy an Azure AI Vision resource using an ARM template, the default network configuration sets the 'defaultAction' to 'Deny' for IP firewall rules, meaning all traffic is blocked unless explicitly allowed. Since you did not configure any network ACLs to permit your application's IP address or virtual network, the 403 Forbidden error occurs because the API endpoint rejects the request at the network layer before any authentication or authorization checks.

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The apiVersion in the template is incorrect

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The custom subdomain name is not configured correctly

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The SKU 'S1' does not support API calls

Question 3

Your team is building a mobile app that uses Azure AI Computer Vision to extract text from business cards. The app must handle cards in multiple languages (English, French, German). Which feature of the Computer Vision API should you use?

Accepted Answer

Read API. The Read API is the correct choice because it is the current, optimized version of the Computer Vision OCR service designed specifically for extracting printed and handwritten text from images of documents, including business cards. It supports multiple languages (English, French, German) and provides a higher accuracy and structured output (lines and words with bounding boxes) compared to the legacy OCR API. The Recognize Text operation is deprecated and should not be used for new solutions.

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OCR API

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Recognize Text

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Describe Image

Question 4

Your retail company uses Azure AI Face API to identify VIP customers when they enter a store. Due to privacy regulations, you must obtain explicit consent and store minimal biometric data. Which approach meets these requirements?

Accepted Answer

Create a PersonGroup with each VIP customer, and use the Identify operation.. The Identify operation in Azure AI Face API uses a PersonGroup to match a detected face against a group of known individuals. This approach stores only the face ID (a unique identifier) rather than the actual biometric data, minimizing data storage and meeting privacy requirements. It also requires explicit consent to enroll individuals in the PersonGroup.

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Train a Custom Vision model with images of VIP customers.

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Use the Face API's Face ID returned upon detection and store it in a database.

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Store the customer's face image in Azure Blob Storage and compare using the Face API.

Question 5

You are building a solution to analyze images of handwritten medical prescriptions. The text is in English and includes drug names and dosages. Which combination of Azure AI services should you use?

Accepted Answer

Azure AI Computer Vision Read API and Azure AI Language. The Azure AI Computer Vision Read API (part of the Image Analysis service) extracts printed and handwritten text from images, including medical prescriptions. Azure AI Language then provides entity recognition to identify drug names and dosages from the extracted text. This combination directly addresses the requirement of analyzing handwritten images and extracting structured medical information.

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Azure AI Custom Vision and Azure AI Language

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Azure AI Document Intelligence and Azure AI Language

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Azure AI Video Indexer and Azure AI Language

Question 6

Refer to the exhibit. You are configuring an Azure AI Video Indexer job. The exhibit shows a JSON snippet of the job configuration. What will Video Indexer extract from the video?

Accepted Answer

OCR text, detected faces, and visual labels. The JSON snippet configures an Azure Video Indexer job with the 'features' array set to include 'ocr', 'faces', and 'labels'. This instructs Video Indexer to extract OCR text (from on-screen text), detected faces (via facial detection and recognition), and visual labels (objects, scenes, and actions). These are the only features specified, so the output will contain exactly those three types of insights.

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Sentiment analysis of spoken content

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Audio transcript and speaker diarization

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Keyframe extraction only

Question 7

Refer to the exhibit. You run the Azure CLI command shown. What is the output?

Accepted Answer

A single key string. The `az cognitiveservices account keys list` command retrieves the access keys for an Azure Cognitive Services account. By default, it returns both key1 and key2, but the `--query` parameter with `keys[0]` filters the output to return only the first key (key1) as a single string. This is a standard Azure CLI query syntax using JMESPath to extract a specific element from the JSON response.

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Both key1 and key2

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The endpoint URL

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The region of the account

Question 8

A retail company uses Azure Computer Vision to analyze in-store camera feeds to count customers. The solution uses the Detect API on individual frames. Recently, the counts have been inaccurate due to overlapping people. Which action should you take to improve accuracy?

Accepted Answer

Train a Custom Vision object detection model with images of crowded scenes.. The Detect API in Azure Computer Vision is a general-purpose object detection model that may not be optimized for crowded scenes with significant overlap. By training a Custom Vision object detection model with images of crowded scenes, you create a specialized model that learns to distinguish partially occluded people, improving counting accuracy. This approach leverages transfer learning to adapt the model to the specific visual patterns of your store environment.

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Use the Analyze API instead of Detect API.

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Increase the confidence threshold in the Detect API call.

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Switch to Azure Video Indexer for people counting.

Question 9

You need to extract printed text from scanned invoices in multiple languages. Which Azure service should you use?

Accepted Answer

Azure Computer Vision Read API. Azure Computer Vision Read API is designed specifically for extracting printed and handwritten text from images and documents, including scanned invoices. It uses optical character recognition (OCR) to handle multiple languages, making it the correct choice for this multilingual text extraction task.

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Azure Form Recognizer

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Azure Custom Vision

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Azure Video Indexer

Question 10

A healthcare provider uses Azure Computer Vision to analyze medical images. They need to ensure patient data is not stored outside the Azure region. What should you configure?

Accepted Answer

Configure a private endpoint for the Computer Vision resource.. Configuring a private endpoint for the Computer Vision resource ensures that all traffic to the service traverses a private IP address within your virtual network, using Azure Private Link. This prevents data from being routed through the public internet and allows you to enforce data residency by keeping all data processing within the designated Azure region, as the private endpoint is deployed in the same region as your VNet.

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Use the Free tier for Computer Vision.

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Enable customer-managed keys (CMK) for the Computer Vision resource.

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Deploy Computer Vision in multiple regions.

Question 11

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 12

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.

Answer

Tags

Question 13

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 14

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 15

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.