DRAG DROP
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You plan to implement an Azure AI Search resource that will use custom skill based on sentiment analysis.
You need to create a custom model and configure Azure AI Search use the model.
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.
1. Create an Azure Machine Learning workspace. 2. Create and train the model in the Azure Machine Learning studio. 3. Create an endpoint for the model. 4. Connect the custom skill to the endpoint. 5. Rerun the indexer to enrich the index.
https://learn.microsoft.com/en-us/training/modules/build-azure-machine-learn-custom-skill-for-azure-cognitive-search/03-enrich-search-index-use-model 1.Create an Azure Machine Learning workspace. 2.Create and train the model in the Azure Machine Learning studio. 3.Create an endpoint for the model. 4.Provision an Azure AI Services resource and obtain the endpoint. 5.Connect the custom skill to the endpoint.
in the link you provided, last step is to update your existing cognitive search that means is to create the search resource first: 1- Provision an Azure AI Services resource and obtain the endpoint. 2- Create an Azure Machine Learning workspace. 3- Create and train the model in the Azure Machine Learning studio. 4- Create an endpoint for the model. 6- Connect the custom skill to the endpoint.
Create workspace Provision Connect Create studio Rerun
Create an Azure Machine Learning workspace. Create and train the model in the Azure Machine Learning studio. Create an endpoint for the model. Connect the custom skill to the endpoint. Rerun the indexer to enrich the index.
Create an Azure Machine Learning workspace. Create and train the model in the Azure Machine Learning studio. Create an endpoint for the model. Provision an Azure AI Services resource and obtain the endpoint. Connect the custom skill to the endpoint.
Based on the detailed steps provided in the Microsoft Learn documentation, the sequence of actions indeed doesn't require provisioning a separate Azure AI Services resource. Instead, the custom skill is directly connected to the Azure Machine Learning model endpoint. https://learn.microsoft.com/en-us/training/modules/build-azure-machine-learn-custom-skill-for-azure-cognitive-search/03-enrich-search-index-use-model
According to GPT4: 1. Create an Azure Machine Learning workspace. 2. Create and train the model in the Azure Machine Learning studio. 3. Provision an Azure AI Services resource and obtain the endpoint. 4. Create an endpoint for the model. Connect the custom skill to the endpoint. Rerun the indexer to enrich the index.
My bad, this is wrong information.
Create an endpoint for the model. Create and train the model in the Azure Machine Learning studio. Create an Azure Machine Learning workspace. Connect the custom skill to the endpoint. so, with the options: Create an Azure Machine Learning Create and train Provision an Azure Create an endpoint Connect
Create an Azure Machine Learning workspace. Create and train the model in the Azure Machine Learning studio. Create an endpoint for the model. Provision an Azure AI Services resource and obtain the endpoint. Connect the custom skill to the endpoint.
Create an Azure Machine Learning workspace. Create and train the model in the Azure Machine Learning studio. Create an endpoint for the model. Provision an Azure AI Services resource and obtain the endpoint. Connect the custom skill the endpoint.
1. Create an Azure Machine Learning workspace. 2. Create and train the model in the Azure Machine Learning studio. 3. Create an endpoint for the model. 4. Connect the custom skill to the endpoint. 5. Rerun the indexer to enrich the index.
1. Create an Azure Machine Learning workspace. 2. Create and train the model in the Azure Machine Learning studio. 3. Create an endpoint for the model. 4. Connect the custom skill to the endpoint. 5. Rerun the indexer to enrich the index.