C is the correct answer.
"To enable active learning, you must log user queries. This is accomplished by calling the endpoint query with the log=true query string parameter and value."
https://docs.microsoft.com/en-us/azure/cognitive-services/LUIS/how-to/improve-application#log-user-queries-to-enable-active-learning
Enabling logging by adding log=true to the prediction endpoint query is necessary to collect data on user interactions. This logged data is used for active learning to improve the model's accuracy by identifying utterances that are not well understood and need further training.
C is the answer
correct answer and reference
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/luis-how-to-review-endpoint-utterances#log-user-queries-to-enable-active-learning
C is the answer.
https://learn.microsoft.com/en-us/azure/cognitive-services/LUIS/how-to/improve-application#log-user-queries-to-enable-active-learning
To enable active learning, you must log user queries. This is accomplished by calling the endpoint query with the log=true query string parameter and value.
Log user queries to enable active learning
To enable active learning, you must log user queries. This is accomplished by calling the endpoint query with the log=true query string parameter and value.
https://learn.microsoft.com/en-us/azure/cognitive-services/LUIS/how-to/improve-application
To enable active learning in a conversational language understanding model, you should add show-all-intents=true to the prediction endpoint query. This will allow you to see all the intents that the model is predicting, including the None intent.[0] This information can be used to improve the model by adding more training data for the None intent or other intents that are not being predicted accurately.
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