Microsoft AI-102 Practice Test - Questions Answers, Page 2

Reference:

https://docs.microsoft.com/en-us/azure/cognitive-services/face/face-api-how-to-topics/use-persondirectory

Reference:

https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/copy-move-projects

Step 1: Create a project

Create a new project.

Step 2: Upload and tag the images

Choose training images. Then upload and tag the images.

Step 3: Train the classifier model.

Train the classifier

Reference:

https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/getting-started-build-a-classifier

Box 1: Classification

Incorrect Answers:

An object detection project is for detecting which objects, if any, from a set of candidates are present in an image.

Box 2: Multiclass

A multiclass classification project is for classifying images into a set of tags, or target labels. An image can be assigned to one tag only.

Incorrect Answers:

A multilabel classification project is similar, but each image can have multiple tags assigned to it.

Box 3: General

General: Optimized for a broad range of image classification tasks. If none of the other specific domains are appropriate, or if you're unsure of which domain to choose, select one of the General domains.

Reference:

https://cran.r-project.org/web/packages/AzureVision/vignettes/customvision.html

B: The Text Analytics API's Sentiment Analysis feature provides two ways for detecting positive and negative sentiment. If you send a Sentiment Analysis request, the API will return sentiment labels (such as "negative", "neutral" and "positive") and confidence scores at the sentence and document-level.

D: The Language Detection feature of the Azure Text Analytics REST API evaluates text input for each document and returns language identifiers with a score that indicates the strength of the analysis.

This capability is useful for content stores that collect arbitrary text, where language is unknown.

Reference:

https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/how-tos/text-analytics-how-to-sentiment-analysis?tabs=version-3-1

https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/how-tos/text-analytics-how-to-language-detection

You might want to manage the translation and localization for the language understanding content for your bot independently.

Translate command in the @microsoft/bf-lu library takes advantage of the Microsoft text translation API to automatically machine translate .lu files to one or more than 60+ languages supported by the Microsoft text translation cognitive service.

What is translated?

An .lu file and optionally translate

Comments in the lu file

LU reference link texts

List of .lu files under a specific path.

Reference:

https://github.com/microsoft/botframework-cli/blob/main/packages/luis/docs/translate-command.md

Obtain values to connect your bot to the knowledge base

1. In the QnA Maker site, select your knowledge base.

2. With your knowledge base open, select the SETTINGS tab. Record the value shown for service name. This value is useful for finding your knowledge base of interest when using the QnA Maker portal interface. It's not used to connect your bot app to this knowledge base.

3. Scroll down to find Deployment details and record the following values from the Postman sample HTTP request:

4. POST /knowledgebases/<knowledge-base-id>/generateAnswer

5. Host: <your-host-url>

6. Authorization: EndpointKey <your-endpoint-key>

Reference:

https://docs.microsoft.com/en-us/azure/bot-service/bot-builder-howto-qna

Reference:

https://github.com/MicrosoftDocs/bot-docs/blob/li ve/arti cles/bot-servi ce-qui ckstart-reqi strati on.md