DRAG DROP

You are building a transcription service for technical podcasts.

Testing reveals that the service fails to transcribe technical terms accurately.

You need to improve the accuracy of the service.

Which five actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order. NOTE: More than one order of answer choices is correct. You will receive credit for any of the correct orders you select.

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Correct Answer:

Explanation:

https://learn.microsoft.com/en-us/azure/cognitive-services/speech-service/custom-speech-overview#how-does-it-work

With Custom Speech, you can upload your own data, test and train a custom model, compare accuracy between models, and deploy a model to a custom endpoint.

– Create a project and choose a model. Use a Speech resource that you create in the Azure portal. If you will train a custom model with audio data, choose a Speech resource region with dedicated hardware for training audio data.

– Upload test data. Upload test data to evaluate the speech to text offering for your applications, tools, and products.

– Train a model. Provide written transcripts and related text, along with the corresponding audio data. Testing a model before and after training is optional but recommended.

– Deploy a model. Once you’re satisfied with the test results, deploy the model to a custom endpoint. With the exception of batch transcription, you must deploy a custom endpoint to use a Custom Speech model.

HOTSPOT

You are building an app by using the Speech SDK. The app will translate speech from French to German by using natural language processing.

You need to define the source language and the output language.

How should you complete the code? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

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Correct Answer:

HOTSPOT

Select the answer that correctly completes the sentence.

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Correct Answer:

DRAG DROP

You are developing a webpage that will use the Video Indexer service to display videos of internal company meetings.

You embed the Player widget and the Cognitive Insights widget into the page.

You need to configure the widgets to meet the following requirements:

✑ Ensure that users can search for keywords.

✑ Display the names and faces of people in the video.

✑ Show captions in the video in English (United States).

How should you complete the URL for each widget? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content. NOTE: Each correct selection is worth one point.

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You have a Language Understanding resource named lu1.

You build and deploy an Azure bot named bot1 that uses lu1.

You need to ensure that bot1 adheres to the Microsoft responsible AI principle of inclusiveness.

How should you extend bot1?

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In Azure Al Studio, you use Completions playground with the GPT-35 Turbo model.

You have a prompt that contains the following code.

You need the model to create an explanation of the code. The solution must minimize costs.

What should you do?

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DRAG DROP

You have an app that manages feedback.

You need to ensure that the app can detect negative comments by using the Sentiment Analysis API in Azure Cognitive Service for Language. The solution must ensure that the managed feedback remains on your company’s internal network.

Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order. NOTE: More than one order of answer choices is correct You will receive credit for any of the correct orders you select.

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Correct Answer:

Explanation:

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://<container IP address>:<port>/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.

You build a Language Understanding model by using the Language Understanding portal.

You export the model as a JSON file as shown in the following sample.

To what does the Weather.Historic entity correspond in the utterance?

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You are designing a content management system.

You need to ensure that the reading experience is optimized for users who have reduced comprehensive and learning differences, such as dyslexia.

Which Azure service should you include in the solution?

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You are designing a conversational interface for an app that will be used to make vacation requests.

The interface must gather the following data:

• The start date of a vacation

• The end date of a vacation

• The amount of required paid time off

The solution must minimize dialog complexity.

Which type of dialog should you use?

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