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NEW QUESTION: 1
Which two are best practices when it comes to component and application event handling? Choose 2 answers
A. Use component events to communicate actions that should be handled at the application level.
B. Reuse the event logic in a component bundle, by putting the logic in the helper. (Missed)
C. Handle low-level events in the event handler and re-fire them as higher-level events. (Missed)
D. Try to use application events as opposed to component events.
Answer: B,C

NEW QUESTION: 2
The Iteration Plan belongs to ______________.
A. The assessment domain and the project management work product kind
B. The planning domain and the project management work product kind
C. The project management domain and the plan work product kind
D. The project management domain the specification work product kind
Answer: C

NEW QUESTION: 3
Several web server virtual machines in your environment are experiencing poor performance. Upon further investigation you learn that one very active database virtual machine is consuming most of your storage bandwidth. You would like to restrict the storage performance of the database VM to increase the performance of the web server VMs. Which of the following features will help you do this?
A. Storage I/O Control
B. Distributed Resource Scheduler (DRS)
C. vMotion
D. Storage vMotion
Answer: A
Explanation:
Reference:http://searchvmware.techtarget.com/definition/VMware-DRS

NEW QUESTION: 4
You need to define an evaluation strategy for the crowd sentiment models.
Which three 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.

Answer:
Explanation:

Explanation:
Scenario:
Experiments for local crowd sentiment models must combine local penalty detection data.
Crowd sentiment models must identify known sounds such as cheers and known catch phrases. Individual crowd sentiment models will detect similar sounds.
Note: Evaluate the changed in correlation between model error rate and centroid distance In machine learning, a nearest centroid classifier or nearest prototype classifier is a classification model that assigns to observations the label of the class of training samples whose mean (centroid) is closest to the observation.
References:
https://en.wikipedia.org/wiki/Nearest_centroid_classifier
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/sweep-clustering