Knowledge check

1.

You want to include a sentiment score for each document in an index. What should you do?

Create a custom skill that uses an Azure Machine Learning model to predict the sentiment for a document

Create a custom skill that calls the Azure AI Language service and predicts the sentiment of each document.

Add the built-in Sentiment skill to the skillset used by the indexer.

2.

You implemented a custom skill as an Azure function. You want to include the custom skill in your Azure AI Search indexing process. What should you do?

Add a WebApiSkill to a skillset, referencing the Azure function's URI

Create a JSON document with the input schema for your function, and save it in the folder where the documents to be indexed are stored.

Submit each document to the function, and store the output in a separate data source. Then use the Merge skill to add the results to the index.

3.

When you create an Azure AI Language project, if you let the model automatically split your training data, what percentage of the documents will it use to train the model, by default?

20%

50%

80%

4.

When you create an Azure Machine Learning custom skill, what type of endpoint does the URI have to use?

The URI has to use an HTTPS endpoint

The URI has to use an HTTP endpoint

The URI has to use an FTP endpoint

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