AI-102 Implement computer vision solutions Practice Test 6 — 15 Questions

Question 1

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?

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

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 2

You are troubleshooting an Azure AI Vision application that calls the Analyze Image API. The application suddenly returns HTTP 403 errors. The API key and endpoint have not changed. What is the most likely cause?

Accepted Answer

The API key has been regenerated or the resource is in a different region.. HTTP 403 errors indicate the server understood the request but refused to authorize it. Since the API key and endpoint have not changed from the client's perspective, the most likely cause is that the key was regenerated in the Azure portal or the resource was moved to a different region, invalidating the previously valid key. This is a common authorization failure distinct from quota or throttling issues.

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The image file size exceeds the maximum limit.

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The service is throttling requests due to high volume.

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The API call quota has been exceeded.

Question 3

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 4

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 5

You need to choose Azure services to build a computer vision pipeline that ingests images from multiple sources, extracts text using OCR, and stores extracted metadata in a Cosmos DB database. Which TWO services should you use?

Accepted Answer

Azure AI Vision. Azure AI Vision (option A) provides the OCR capability needed to extract text from images via its Read API, which is the core requirement of the pipeline. Azure Functions (option C) is the correct compute service to orchestrate the ingestion, call the OCR API, and write the extracted metadata to Cosmos DB, as it supports event-driven triggers and can scale with image volume. Together, they form a serverless pipeline that meets the stated requirements.

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Azure Cognitive Search

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Azure Logic Apps

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Azure Blob Storage

Question 6

Your organization uses Azure AI Vision to analyze surveillance images for security threats. The solution must comply with GDPR. Which TWO actions should you take?

Accepted Answer

Disable logging of request and response data.. Disabling logging of request and response data is a direct measure to minimize personal data processing, which aligns with GDPR's data minimization principle. Azure AI Vision logs can contain image metadata and analysis results that may include personal data, so disabling logging reduces GDPR compliance risk. Option E is correct because GDPR requires that personal data of EU residents be stored within the EU, and configuring a specific data residency region ensures compliance with this requirement.

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Enable geo-redundant storage for the Azure AI Vision resource.

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Enable Customer-Managed Keys (CMK) for encryption.

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Use private endpoints to access the service.

Question 7

You are developing a solution to detect and redact personally identifiable information (PII) in images of documents. Which THREE Azure services can you use together?

Accepted Answer

Azure AI Language (PII detection). Options B, C, and D are correct because Azure AI Document Intelligence and Azure AI Vision (Read API) can extract text from images of documents, and Azure AI Language (PII detection) can identify PII in the extracted text, enabling redaction. Option A (Custom Vision) is incorrect because it is designed for image classification and object detection, not PII detection. Option E (Video Indexer) is incorrect because it is for analyzing videos, not static images.

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Azure Custom Vision

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Azure Video Indexer

Question 8

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 9

Refer to the exhibit. You are using the Azure AI Face API to detect faces in an image. You need to ensure that the response includes the unique face ID for each detected face. However, the response does not contain face IDs. What is the most likely cause?

Accepted Answer

The 'detectionModel' parameter is not set; the default detection model does not return face IDs.. The Face API's default detection model (detection_01) does not return face IDs. To obtain face IDs, you must explicitly set the 'detectionModel' parameter to 'detection_03' or 'detection_04', which support face ID generation. Without this parameter, the API omits the face ID field in the response.

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The 'returnFaceLandmarks' parameter must be set to true.

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The 'returnFaceId' parameter is set to false.

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The API version is incorrect; use '2023-06-01-preview' instead.

Question 10

You are building an application that needs to extract printed text from scanned invoices. Which Azure AI service should you use?

Accepted Answer

Azure AI Vision OCR. Azure AI Vision OCR is the correct choice because it is specifically designed to extract printed text from images and scanned documents using optical character recognition (OCR). The OCR API within Azure AI Vision can detect and extract text from invoices, signs, and other printed materials, returning the text along with bounding box coordinates. This service is optimized for printed text extraction, making it ideal for this use case.

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Azure AI Document Intelligence

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Azure AI Search

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Azure AI Language

Question 11

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 12

You are deploying a custom image classification model using Azure AI Custom Vision. The model must achieve high accuracy on a dataset with subtle differences between classes. However, the training set is small (200 images per class). Which strategy should you use to improve model performance?

Accepted Answer

Use transfer learning with a pre-trained model and fine-tune. Transfer learning with a pre-trained model (e.g., ResNet, EfficientNet) is ideal for small datasets because it leverages features learned from large-scale datasets like ImageNet. Fine-tuning adjusts only the final layers to the new task, preventing overfitting and achieving high accuracy even with subtle inter-class differences. Custom Vision automatically uses transfer learning, making option D the correct strategy.

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Enable hyperparameter tuning and increase the number of iterations

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Apply aggressive data augmentation and train from scratch

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Use a larger batch size and train for more epochs

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