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NEW QUESTION: 1

A. Option D
B. Option F
C. Option E
D. Option A
E. Option B
F. Option C
Answer: E,F
Explanation:
B: Trace log, level Monitorable. This level records messages about all unrecoverable events that limit the functionality of the solution but do not stop the application. When set to this level, the log also includes events that the Unexpected setting records.
C: Event log, Information: Information messages do not require any action. However, they can provide valuable data for monitoring the state of your solution.
Reference: Configure diagnostic logging in SharePoint 2013
https://technet.microsoft.com/en-us/library/ee748656.aspx

NEW QUESTION: 2
Which command can be used to troubleshoot an IPv6 FlexVPN spoke-to-hub connectivity failure?
A. show crypto isakmp sa
B. show crypto lkev2 client flexvpn
C. show crypto identity
D. show crypto gkm
Answer: B

NEW QUESTION: 3
Note: This question is part of a series of questions that present the same scenario. Each ques-tion on the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You need to design the system that handles uploaded documents.
Solution: Use an Azure Blob Container as the location to upload documents. Use Azure Service Bus for user notification and to start processing.
Does the solution meet the goal?
A. No
B. Yes
Answer: B
Explanation:
Explanation/Reference:
Explanation:
An Azure Blob Container, which uses an object store with flat namespace, is good for this scenario.
A service bus is needed to meet the requirements.
Scenario: Document Uploads
During the document upload process, the solution must capture information about the geographic location where documents originate. Processing of documents must be automatically triggered when documents are uploaded. Customers must be notified when analysis of their uploaded documents begins.
Uploaded documents must be processed using Azure Machine Learning Studio in an Azure Data Factory pipeline. The machine learning portion of the pipeline is updated once a quarter.
When document processing is complete, the documents and the results of the analysis process must be visible.
Reference: https://docs.microsoft.com/en-us/azure/event-grid/compare-messaging-services