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Microsoft Azure AI Fundamentals: Explore computer vision Microsoft Quiz Answers
Get Microsoft Azure AI Fundamentals: Explore computer vision Microsoft Quiz Answers
Computer vision is an area of artificial intelligence (AI) in which software systems are designed to perceive the world visually, though cameras, images, and video. There are multiple specific types of computer vision problem that AI engineers and data scientists can solve using a mix of custom machine learning models and platform-as-a-service (PaaS) solutions – including many cognitive services in Microsoft Azure.
This learning path helps prepare you for Exam AI-900: Microsoft Azure AI Fundamentals.
Prerequisites:
Ability to navigate the Azure portal
Module 1: Analyze images with the Computer Vision service
The Computer Vision service enables software engineers to create intelligent solutions that extract information from images; a common task in many artificial intelligence (AI) scenarios.
Learning objectives:
Learn how to use the Computer Vision cognitive service to analyze images.
Prerequisites:
Ability to navigate the Azure portal
Quiz 1: Knowledge check
Q1. You want to use the Computer Vision service to analyze images. You also want to use the Language service to analyze text. You want developers to require only one key and endpoint to access all of your services. What kind of resource should you create in your Azure subscription?
- Computer Vision
- Cognitive Services
- Custom Vision
Q2. You want to use the Computer Vision service to identify the location of individual items in an image. Which of the following features should you retrieve?
- Objects
- Tags
- Categories
Q3. You want to use the Computer Vision service to analyze images of locations and identify well-known buildings. What should you do?
- Retrieve the objects in the image.
- Retrieve the categories for the image, specifying the celebrities domain
- Retrieve the categories for the image, specifying the landmarks domain
Module 2: Classify images with the Custom Vision service
Image classification is a common workload in artificial intelligence (AI) applications. It harnesses the predictive power of machine learning to enable AI systems to identify real-world items based on images.
Learning objectives:
Learn how to use the Custom Vision service to create an image classification solution.
Prerequisites:
Ability to navigate the Azure portal
Quiz 1: Knowledge check
Q1. You plan to use the Custom Vision service to train an image classification model. You want to create a resource that can only be used for model training, and not for prediction. Which kind of resource should you create in your Azure subscription?
- Custom Vision
- Cognitive Services
- Computer Vision
Q2. You train an image classification model that achieves less than satisfactory evaluation metrics. How might you improve it?
- Reduce the size of the images used to train the model.
- Add a new label for “unknown” classes.
- Add more images to the training set.
Q3. You have published an image classification model. What information must you provide to developers who want to use it?
- Only the project ID.
- The project ID, the model name, and the key and endpoint for the prediction resource
- The project ID, iteration number, and the key and endpoint for the training resource.
Module 3: Detect objects in images with the Custom Vision service
Object detection is a form of computer vision in which artificial intelligence (AI) agents can identify and locate specific types of object in an image or camera feed.
Learning objectives:
Learn how to use the Custom Vision service to create an object detection solution.
Prerequisites:
Ability to navigate the Azure portal
Quiz 1: Knowledge check
Q1. Which of the following results does an object detection model typically return for an image?
- A class label and probability score for the image
- Bounding box coordinates that indicates the region of the image where all of the objects it contains are located
- A class label, probability, and bounding box for each object in the image
Q2. You plan to use a set of images to train an object detection model, and then publish the model as a predictive service. You want to use a single Azure resource with the same key and endpoint for training and prediction. What kind of Azure resource should you create?
- Cognitive Services
- Custom Vision
- Computer Vision
Module 4: Detect and analyze faces with the Face service
Face detection, analysis, and recognition are important capabilities for artificial intelligence (AI) solutions. The Face cognitive service in Azure makes it easy integrate these capabilities into your applications.
Learning objectives:
Learn how to use the Face cognitive service to detect and analyze faces in images.
Prerequisites:
Ability to navigate the Azure portal
Quiz 1: Knowledge check
Q1. You plan to use Face to detect human faces in an image. How does the service indicate the location of the faces it detects?
- A pair of coordinates for each face, indicating the center of the face
- Two pairs of coordinates for each face, indicating the location of the eyes
- A set of coordinates for each face, defining a rectangular bounding box around the face
Q2. What is one aspect that may impair facial detection?
- Glasses
- Extreme angles
- Fast shutter speed
Module 5: Read text with the Computer Vision service
Optical character recognition (OCR) enables artificial intelligence (AI) systems to read text in images, enabling applications to extract information from photographs, scanned documents, and other sources of digitized text.
Learning objectives:
Learn how to read text in images with the Computer Vision service
Prerequisites:
Ability to navigate the Azure portal
Quiz 1: Knowledge check
Q1. You want to extract text from images and then use the Text Analytics service to analyze the text. You want developers to require only one key and endpoint to access all of your services. What kind of resource should you create in your Azure subscription?
- Computer Vision
- Cognitive Services
- Text Analytics
Q2. You plan to use the Computer Vision service to read text in a large PDF document. Which API should you use?
- The Read API
- The OCR API
- The Recognize Text API
Module 6: Analyze receipts with the Form Recognizer service
Processing invoices and receipts is a common task in many business scenarios. Increasingly, organizations are turning to artificial intelligence (AI) to automate data extraction from scanned receipts.
Learning objectives:
Learn how to use the built-in receipt processing capabilities of the Form Recognizer service
Prerequisites:
Ability to navigate the Azure portal
Quiz 1: Knowledge check
Q1. You plan to use the Form Recognizer pre-built receipt model. Which kind of Azure resource should you create?
- Computer Vision resource
- Form Recognizer or Cognitive Services resource
- Only Form Recognizer resource
Q2. You are using the Form Recognizer service to analyze receipts that you have scanned into JPG format images. What is the maximum file size of JPG file you can submit to the pre-built receipt model?
- 2 MB
- 50 MB
- 200 MB
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