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Microsoft Designing and Implementing a Microsoft Azure AI Solution Sample Questions (Q69-Q74):

NEW QUESTION # 69
You successfully run the following HTTP request.
POST
https://management.azure.com/subscriptions/18c51a87-3a69-47a8-aedc-a54745f708a1/resourceGroups/RG1/pro Body{"keyName": "Key2"} What is the result of the request?

A. A new query key was generated.
B. The primary subscription key and the secondary subscription key were rotated.
C. The secondary subscription key was reset.
D. A key for Azure Cognitive Services was generated in Azure Key Vault.

Answer: A

Explanation:
Explanation
Accounts - Regenerate Key regenerates the specified account key for the specified Cognitive Services account.
Syntax:
POST https://management.azure.com/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/ providers/Microsoft.CognitiveServices/accounts/{accountName}/regenerateKey?api-version=2017-04-18 Reference:
https://docs.microsoft.com/en-us/rest/api/cognitiveservices/accountmanagement/accounts/regeneratekey

NEW QUESTION # 70
You are developing the knowledgebase by using Azure Cognitive Search.
You need to build a skill that will be used by indexers.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Reference:
https://docs.microsoft.com/en-us/azure/search/cognitive-search-skill-entity-recognition

NEW QUESTION # 71
You are building a solution that will detect anomalies in sensor data from the previous 24 hours.
You need to ensure that the solution scans the entire dataset, at the same time, for anomalies.
Which type of detection should you use?

A. streaming
B. change point
C. batch

Answer: C

Explanation:
Explanation
Batch anomaly detection is a type of anomaly detection that scans the entire dataset at once for outliers and unusual patterns. Batch anomaly detection is suitable for offline analysis of historical data, such as sensor data from the previous 24 hours. Batch anomaly detection can use various techniques, such as statistical methods, machine learning methods, or hybrid methods, to identify anomalies in the data123.

NEW QUESTION # 72
Select the answer that correctly completes the sentence.

Answer:

Explanation:

Explanation

NEW QUESTION # 73
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.

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.

NEW QUESTION # 74
......

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Guaranteed AI-102 Passing, AI-102 Examcollection

2025 AI-102: The Best Guaranteed Designing and Implementing a Microsoft Azure AI Solution Passing

Microsoft Designing and Implementing a Microsoft Azure AI Solution Sample Questions (Q69-Q74):

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