You have created example utterances and intents for your LUIS application. You train your application and while you review the dashboard, you identify certain issues. Your top intent and the next intent scores are close enough that may flip with the next training. To address the issue, you decide to delete a number of utterances amongst different intents. This changes the quantity of example utterances significantly. With this change, your review dashboard analysis again. What kind of issue wouldyou expect on the dashboard with the new change?

Answer options:

A.Incorrect predictions
B.Unclear predictions
C.Data imbalance
D.None of the above

Correct Answer: C
Option A is incorrect because incorrect predictions occur when example utterance is not predicted for the labeled intent. Instead, it is predicted for a different intent. To remediate this issue, you would need to edit the utterances to be more specific and train the model again.
Option B is incorrect because unclear predictions occur when top intent and next intent scores are close enough to flip the results with the next model training. To remediate this issue, you would need to combine the intents or edit the utterances and train the model again.
Option C is correct because data imbalance occurs when the quantity of example utterances varies significantly. To remediate this issue, you would need to add more utterances to the intent and train the model again.
Reference:
To learn more about issues that can be fixed using dashboard analysis, use the link given below: 

https://docs.microsoft.com/en-us/azure/cognitive-services/luis/luis-how-to-use-dashboard