You need to build a solution that will use optical character recognition (OCR) to scan sensitive documents by using the Computer Vision API. The solution must NOT be deployed to the public cloud. What should you do?
Answer(s): B
One option to manage your Computer Vision containers on-premises is to use Kubernetes and Helm.Three primary parameters for all Cognitive Services containers are required. The Microsoft Software License Terms must be present with a value of accept. An Endpoint URI and API key are also needed.Incorrect:Not D: This Computer Vision endpoint would be available for the public, unless it is secured.
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/deploy-computer-vision-on-premises
You have an Azure Cognitive Search solution and a collection of handwritten letters stored as JPEG files. You plan to index the collection. The solution must ensure that queries can be performed on the contents of the letters. You need to create an indexer that has a skillset. Which skill should you include?
The Optical character recognition (OCR) skill recognizes printed and handwritten text in image files.An OCR skill uses the machine learning models provided by Computer Vision API v3.2 in Cognitive Services.The OCR skill extracts text from image files. Supported file formats include:.JPEG.JPG.PNG.BMP.TIFF
https://learn.microsoft.com/en-us/azure/search/cognitive-search-skill-ocr
HOTSPOT (Drag and Drop is not supported)You have a library that contains thousands of images. You need to tag the images as photographs, drawings, or clipart. Which service endpoint and response property should you use? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.
Answer(s): A
Box 1: Custom Vision image classificationYou can use a machine learning classification technique to predict which category, or class, something belongs to. Classification machine learning models use a set of inputs, which we call features, to calculate a probability score for each possible class and predict a label that indicates the most likely class that an object belongs to.Box 2: categories
https://learn.microsoft.com/en-us/training/modules/classify-images-custom-vision/1a-overview-classification
You have an app that captures live video of exam candidates. You need to use the Face service to validate that the subjects of the videos are real people. What should you do?
Face rectangleEach detected face corresponds to a faceRectangle field in the response. This is a set of pixel coordinates for the left, top, width, and height of the detected face. Using these coordinates, you can get the location and size of the face. In the API response, faces are listed in size order from largest to smallest.
https://learn.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-face-detection
HOTSPOT (Drag and Drop is not supported)You make an API request and receive the results shown in the following exhibits. Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic. NOTE: Each correct selection is worth one point.
Box 1: detects* Face IDThe face ID is a unique identifier string for each detected face in an image.* Face rectangleEach detected face corresponds to a faceRectangle field in the response. This is a set of pixel coordinates for the left, top, width, and height of the detected face. Using these coordinates, you can get the location and size of the face. In the API response, faces are listed in size order from largest to smallest.Box 2: 707, 201The only detect face with QualityForRecognition high has the attributes left 797 and top 201.* QualityForRecognition - The overall image quality regarding whether the image being used in the detection is of sufficient quality to attempt face recognition on. The value is an informal rating of low, medium, or high. Only "high" quality images are recommended for person enrollment, and quality at or above "medium" is recommended for identification scenarios.
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