AI-102 Dumps with Practice Exam Questions Answers

Graphical user interface, text, application, email Description automatically generated

Box 1: Yes

The Key Phrase Extraction API evaluates unstructured text, and for each JSON document, returns a list of key phrases.

Box 2: No

'the' is not a key phrase.

This capability is useful if you need to quickly identify the main points in a collection of documents. For example, given input text "The food was delicious and there were wonderful staff", the service returns the main talking points: "food" and "wonderful staff".

Box 3: No

Key phrase extraction does not have confidence levels.

• Provision the Language service resource in Azure.
• Deploy a Docker container to an on-premises server.
• Run the container and query the prediction endpoint.

• According to the Microsoft documentation, the Language service is a cloud-based service that provides various natural language processing features, such as sentiment analysis, key phrase extraction, named entity recognition, etc. You can provision the Language service resource in Azure by following the steps in Create a Language resource. You will need to provide a name, a subscription, a resource group, a region, and a pricing tier for your resource. You will also get a key and an endpoint for your resource, which you will use to authenticate your requests to the Language service API.
• According to the Microsoft documentation, you can also use the Language service as a container on your own premises or in another cloud. This option gives you more control over your data and network, and allows you to use the Language service without an internet connection. You can deploy a Docker container to an on-premises server by following the steps in Deploy Language containers. You will need to have Docker installed on your server, pull the container image from the Microsoft Container Registry, and run the container with the appropriate parameters. You will also need to activate your container with your key and endpoint from your Azure resource.
• According to the Microsoft documentation, once you have deployed and activated your container, you can run it and query the prediction endpoint to get sentiment analysis results. The prediction endpoint is a local URL that follows this format: http://:/text/analytics/v3.1-preview.4/sentiment. You can send HTTP POST requests to this endpoint with your text input in JSON format, and receive JSON responses with sentiment labels and scores for each document and sentence in your input.