AI-102 Implement computer vision solutions Practice Test 7 — 15 Questions

Question 1

You have an Azure AI Vision resource named MyVisionService. You run the above Azure CLI command and get the keys. Your application uses key1 for authentication. You need to rotate the keys without downtime. What should you do?

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

Accepted Answer

Update the application to use key2, then regenerate key1. It enables key rotation without downtime. By first updating the application to use key2 (the secondary key), you ensure that authentication continues to work while key1 is being regenerated. After key1 is regenerated, you can optionally update the application back to key1 at a later time. This pattern is standard for Azure Cognitive Services to maintain continuous access.

Answer

Delete and recreate the Cognitive Services resource

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Regenerate key1 immediately and update the application to use the new key1

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Regenerate both keys at the same time

Question 2

You are developing an application that processes images of handwritten forms. The forms contain checkboxes that may be checked or unchecked. Which Azure AI service should you use to detect the state of the checkboxes?

Accepted Answer

Azure AI Document Intelligence. Azure AI Document Intelligence (formerly Form Recognizer) is the correct service because it is specifically designed to extract structured data from documents, including detecting the state of checkboxes (checked or unchecked) in forms. It uses prebuilt models like the 'prebuilt-document' or custom extraction models to analyze form fields and checkbox selections, making it the optimal choice for this task.

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Azure AI Custom Vision

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Azure AI Language

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Azure AI Computer Vision

Question 3

A manufacturing company uses Azure AI Custom Vision to detect defects on a production line. The model was trained with 500 images per class and achieves 95% accuracy. After deployment, the model's accuracy drops to 80% due to changes in lighting conditions. What is the most effective first step to improve the model's robustness?

Accepted Answer

Capture additional images under the new lighting and retrain the model.. The drop in accuracy is caused by a domain shift—specifically, new lighting conditions that were not represented in the original training set. The most effective first step is to capture additional images under the new lighting and retrain the model, as Custom Vision relies on diverse, representative training data to generalize to real-world variations. This directly addresses the root cause by expanding the training distribution to include the new lighting scenario, which is a fundamental principle of supervised learning in computer vision.

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Reduce the probability threshold to increase recall.

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Use Azure AutoML to automatically find the best algorithm.

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Add more images from the original lighting conditions to the training set.

Question 4

You need to analyze a video stream from a security camera to count the number of people entering a building. Which Azure AI service is most suitable?

Accepted Answer

Azure AI Spatial Analysis. Azure AI Spatial Analysis is the most suitable service because it is purpose-built for real-time spatial analysis of video streams, including counting people entering a building. It can process live camera feeds and detect when people cross a line or enter a zone, providing real-time counts. In contrast, Azure AI Video Indexer is designed for analyzing recorded video files, not live streams, making it less suitable for real-time security camera analysis. Other options like Custom Vision and Computer Vision are for image classification and image analysis, respectively, not optimized for video streams.

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Azure AI Custom Vision

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Azure AI Computer Vision

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Azure AI Video Indexer

Question 5

You are deploying a Custom Vision object detection model to an Azure Container Instance for real-time inference. The model must respond within 500 ms. The default container runs on CPU. What should you do to meet the latency requirement?

Accepted Answer

Export the model as a Dockerfile with GPU support and deploy to a GPU-enabled ACI.. The default Custom Vision container runs on CPU, which is insufficient for real-time object detection inference within 500 ms. Exporting the model as a Dockerfile with GPU support and deploying to a GPU-enabled Azure Container Instance (ACI) leverages NVIDIA CUDA-accelerated inference, dramatically reducing latency to meet the sub-500 ms requirement.

Answer

Increase the number of CPU cores in the container instance.

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Deploy the model to Azure Functions with a Premium plan.

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Use the Cognitive Services Computer Vision container instead.

Question 6

You need to detect if a photo contains adult or racy content. Which Azure AI Computer Vision feature should you use?

Accepted Answer

Analyze Image API with the 'adult' parameter. The Analyze Image API with the 'adult' parameter is the correct feature because it specifically detects adult, racy, and gory content in images. When you call the Analyze Image API and include the 'adult' visual feature, Azure AI Computer Vision returns a boolean flag and a confidence score for adult and racy content classification, enabling content moderation.

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Describe Image API

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OCR API

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Tag Image API

Question 7

You are using Azure AI Custom Vision to classify images of animals. The training set has 1000 images of cats and 1000 images of dogs. After training, the model performs well on the test set. However, when deployed, it misclassifies images of wolves as dogs. What is the most likely cause?

Accepted Answer

The training set does not include enough negative examples that look like dogs but are not.. The model misclassifies wolves as dogs because the training set lacks negative examples that are visually similar to dogs but belong to a different class. Custom Vision learns to distinguish classes based on the features present in the training images; without images of wolf-like canines labeled as 'not dog,' the model has no basis to reject wolves. This is a classic case of insufficient hard negative mining, where the model generalizes too broadly for the 'dog' class.

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The probability threshold is set too low.

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The model is overfitted to the training data.

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The training set has class imbalance.

Question 8

Which TWO actions should you take to reduce the latency of an Azure AI Computer Vision OCR call on a large image?

Accepted Answer

Resize the image to a smaller resolution before calling the API.. Options B and D are correct. B: Resizing reduces processing time. D: Using the Read API asynchronously allows the client to poll, avoiding timeout. A: Increasing timeout doesn't reduce latency. C: Using CPU doesn't help. E: Cognitive Services container on-premises might add network latency.

Answer

Use a CPU-bound compute instance.

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Increase the API timeout value.

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Deploy the Cognitive Services container on-premises.

Question 9

Which THREE factors are critical to consider when designing a custom vision solution for a manufacturing quality inspection system?

Accepted Answer

Imbalance between defective and non-defective product samples.. Class imbalance is a critical factor in custom vision solutions for manufacturing quality inspection. If defective samples are rare compared to non-defective ones, the model may become biased toward predicting the majority class, leading to poor recall for defects. Azure Custom Vision allows adjusting the probability threshold and using techniques like oversampling or weighted loss to mitigate this, but the imbalance must be accounted for during dataset preparation.

Answer

The need for optical character recognition (OCR) of product serial numbers.

Answer

Multilingual support for labeling.

Question 10

Which TWO Azure AI services can be used to extract text from images?

Accepted Answer

Azure AI Document Intelligence. Azure AI Document Intelligence (formerly Form Recognizer) includes the Read OCR engine that extracts printed and handwritten text from images and documents. Azure AI Computer Vision provides the OCR API (optical character recognition) which can extract text from images, including both printed and handwritten text, and supports multiple languages. Both services are designed specifically for text extraction from visual content.

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Azure AI Face

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Azure AI Video Indexer

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Azure AI Custom Vision

Question 11

Refer to the exhibit. You are creating an Azure Cognitive Services account using an ARM template snippet. What type of account is being created?

Accepted Answer

Azure AI Services multi-service account. The ARM template snippet uses the 'CognitiveServices' resource type and sets 'kind' to 'CognitiveServices', which provisions a multi-service account that provides access to multiple Azure AI services (e.g., Language, Computer Vision, Translator) under a single endpoint and key. This is distinct from single-service accounts, which use specific 'kind' values like 'TextAnalytics' or 'ComputerVision'.

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Azure AI Language

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Azure AI Computer Vision

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Azure OpenAI Service

Question 12

You are building a computer vision solution to detect defects on a manufacturing assembly line. The solution must process images in real-time with low latency, and you need to choose an Azure service. Which 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 or object detection model tailored to detect specific manufacturing defects. It supports real-time, low-latency inference via a Docker container deployed to edge devices or directly through the prediction API, meeting the assembly line's performance requirements.

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Azure Computer Vision API

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Azure Video Indexer

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Azure Form Recognizer

Question 13

You are designing a solution to detect brand logos in social media images. The logos vary in size and orientation. You need to achieve high accuracy with minimal false positives. Which approach should you recommend?

Accepted Answer

Train an Azure Custom Vision object detection model with labeled logo images.. Azure Custom Vision allows you to train a custom object detection model with your own labeled dataset of brand logos, enabling high accuracy for specific logo shapes, sizes, and orientations. This approach directly addresses the need for minimal false positives by learning the exact visual features of the logos, unlike generic pre-built models.

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Use Azure Computer Vision Describe API to generate captions and filter by logo mentions.

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Use Azure Computer Vision Analyze API with domain-specific models.

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Use Azure Form Recognizer to extract logo positions from images.

Question 14

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 15

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