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NEW QUESTION: 1
You create an Azure file sync group named Sync 1 and perform the following actions:
* Add share as the cloud endpoint for Sync1.
* Add data1 as a server endpoint for Sync1.
* Register Server1 and Server2 to Sync1.
For each of the following statements, select Yes if the statement is true Otherwise, select No.
NOTE Each correct selection is worth one point.
Answer:
Explanation:
Explanation
NEW QUESTION: 2
When are packets processed by an inbound access list?
A. after they are routed to an outbound interface
B. before and after they are routed to an outbound interface
C. after they are routed to an outbound interface but before being placed in the outbound queue
D. before they are routed to an outbound interface
Answer: D
NEW QUESTION: 3
You are solving a classification task.
The dataset is imbalanced.
You need to select an Azure Machine Learning Studio module to improve the classification accuracy.
Which module should you use?
A. Synthetic Minority Oversampling Technique (SMOTE)
B. Permutation Feature Importance
C. Fisher Linear Discriminant Analysis.
D. Filter Based Feature Selection
Answer: A
Explanation:
Explanation
Use the SMOTE module in Azure Machine Learning Studio (classic) to increase the number of underepresented cases in a dataset used for machine learning. SMOTE is a better way of increasing the number of rare cases than simply duplicating existing cases.
You connect the SMOTE module to a dataset that is imbalanced. There are many reasons why a dataset might be imbalanced: the category you are targeting might be very rare in the population, or the data might simply be difficult to collect. Typically, you use SMOTE when the class you want to analyze is under-represented.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote
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