AI-102 Implement computer vision solutions Practice Test 8 — 15 Questions

Question 1

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 2

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 3

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 4

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 5

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 6

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 7

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.

Question 8

Your team is building a mobile app that uses Azure Custom Vision to classify plant species. The app must work offline and sync labeled images when connectivity is restored. Which SDK feature should you use?

Accepted Answer

Export the model as a TensorFlow or CoreML model for on-device inference. Azure Custom Vision allows exporting trained models to formats like TensorFlow, CoreML, ONNX, or Docker for on-device inference. This enables the mobile app to run classification locally without network connectivity, and the Custom Vision SDK includes a method to upload labeled images for offline training sync when connectivity is restored.

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Azure IoT Edge runtime on the phone

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Azure API Management with caching

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Continuous deployment integration

Question 9

A manufacturing company uses Azure Custom Vision to detect defects on an assembly line. The model is deployed to a container on a local edge server. Recently, the model's accuracy dropped. You suspect data drift. What should you do to monitor and retrain the model?

Accepted Answer

Periodically collect new images with labels, retrain the model in Custom Vision, and redeploy the updated container.. Custom Vision models deployed to containers on edge devices do not expose a REST endpoint that Azure Machine Learning's data drift monitoring can directly access. The only way to detect drift and retrain is to periodically collect new labeled images from the production line, retrain the model in Custom Vision, and redeploy the updated container to the edge server.

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Use Azure Machine Learning data drift monitoring on the Custom Vision endpoint.

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Configure Custom Vision to send alerts when drift is detected.

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Enable active learning in Custom Vision to automatically retrain the model.

Question 10

You are building a solution to automatically tag images uploaded to an Azure Storage blob container using Azure AI Vision. The solution must process images as soon as they are uploaded. Which service should you use to trigger the image analysis?

Accepted Answer

Azure Event Grid with an Azure Function trigger. Azure Event Grid is the correct choice because it provides a serverless event-driven architecture that can react to blob storage events (e.g., BlobCreated) in near real-time. By configuring an Event Grid subscription on the storage account, you can trigger an Azure Function that uses Azure AI Vision to analyze the image as soon as it is uploaded, without polling or scheduled checks.

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Azure Functions with a timer trigger

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Azure Batch with a job schedule

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Azure Logic Apps with a recurrence trigger

Question 11

Refer to the exhibit. You have trained an object detection model in Azure Custom Vision. The model is published as 'defect-model'. You need to deploy this model to a Docker container for on-premises inference using the Azure IoT Edge runtime. What should you do first?

Accepted Answer

Export the model as a Docker container (e.g., TensorFlow) using the Custom Vision portal.. To deploy a Custom Vision model to an Azure IoT Edge device, you must first export the model as a Docker container (e.g., TensorFlow, ONNX, or DockerFile) from the Custom Vision portal. This export creates a container image that can be deployed to Azure Container Registry and then used as a module in an IoT Edge deployment. Without this export step, you cannot create the containerized module required for on-premises inference.

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Create an Azure Container Registry and push the Custom Vision base image.

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Use the Custom Vision prediction API to call the published endpoint from the edge device.

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Retrain the model with more images to improve mAP.

Question 12

You have a computer vision solution that analyzes security camera feeds to detect people and vehicles. The solution uses Azure AI Vision Spatial Analysis. You need to ensure compliance with privacy regulations by blurring detected faces. Which feature should you enable?

Accepted Answer

Enable face blurring in the Spatial Analysis configuration. Azure AI Vision Spatial Analysis includes a built-in face blurring feature that can be enabled directly in the Spatial Analysis configuration. This allows you to automatically blur detected faces in the video feed at the edge or in the cloud, ensuring compliance with privacy regulations without requiring additional services or post-processing steps.

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Use Azure AI Content Safety to filter faces

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Post-process frames with Azure AI Face client SDK

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Enable face detection and redact faces using Azure AI Video Indexer

Question 13

Your application needs to determine whether two photos of the same person are of the same individual, even if they are from different angles. Which Azure AI service should you use?

Accepted Answer

Azure AI Face. Azure AI Face provides face verification APIs that compare two faces and return a confidence score indicating whether they belong to the same person. It uses deep learning models trained to handle variations in pose, lighting, and expression, making it ideal for matching photos of the same individual from different angles.

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Azure AI Video Indexer

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Azure AI Custom Vision

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Azure AI Vision OCR

Question 14

You are designing a solution that reads handwritten notes from patient intake forms. The solution must handle various handwriting styles. Which Azure AI capability should you use?

Accepted Answer

Azure AI Document Intelligence Read model. Azure AI Document Intelligence Read model is specifically designed to extract printed and handwritten text from documents, including patient intake forms. It uses advanced OCR capabilities optimized for varied handwriting styles and document layouts, making it the correct choice for this scenario.

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Azure AI Custom Vision

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Azure AI Vision OCR

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Azure AI Language

Question 15

You have a real-time video processing pipeline using Azure AI Video Indexer. You need to detect when a specific person appears in archived video footage. Which approach minimizes latency and cost?

Accepted Answer

Use Video Indexer's face detection and indexing, then search. Video Indexer's built-in face detection and indexing automatically identifies and tracks faces during the indexing process, storing the results in a searchable metadata index. To detect when a specific person appears, you can then search the indexed metadata for that person's face ID or name, which avoids re-processing the video and minimizes both latency and cost. This approach leverages the one-time indexing cost and optimized search capabilities rather than running additional AI services on every frame.

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Extract keyframes and use Custom Vision to detect the person

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Run face detection on every frame using Azure AI Face and store results

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Use Azure AI Vision to detect faces in video frames and compare against a database