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NEW QUESTION: 1
When a learn member encounters an issue in an agile environment, what should they do?
A. Facilitate one-on-one communication between team members to reduce conflict and inefficiencies
B. Manage communication between ail team members and the customer to promote effective and transparent collaboration
C. Manage communication between a few team members and the customer so that they may convey information to other team members
D. Limit communication between the team members and the customer to prevent unnecessary anxiety
Answer: A

NEW QUESTION: 2
You implement an event processing solution using Microsoft Azure Stream Analytics.
The solution must meet the following requirements:
*Ingest data from Blob storage
* Analyze data in real time
*Store processed data in Azure Cosmos DB
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

Step 1: Configure Blob storage as input; select items with the TIMESTAMP BY clause The default timestamp of Blob storage events in Stream Analytics is the timestamp that the blob was last modified, which is BlobLastModifiedUtcTime. To process the data as a stream using a timestamp in the event payload, you must use the TIMESTAMP BY keyword.
Example:
The following is a TIMESTAMP BY example which uses the EntryTime column as the application time for events:
SELECT TollId, EntryTime AS VehicleEntryTime, LicensePlate, State, Make, Model, VehicleType, VehicleWeight, Toll, Tag FROM TollTagEntry TIMESTAMP BY EntryTime Step 2: Set up cosmos DB as the output Creating Cosmos DB as an output in Stream Analytics generates a prompt for information as seen below.

Step 3: Create a query statement with the SELECT INTO statement.
References:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-define-inputs

NEW QUESTION: 3
You have a new Azure Data Factory environment.
You need to periodically analyze pipeline executions from the last 60 days to identify trends in execution durations. The solution must use Azure Log Analytics to query the data and create charts.
Which diagnostic settings should you configure in Data Factory? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation

Log type: PipelineRuns
A pipeline run in Azure Data Factory defines an instance of a pipeline execution.
Storage location: An Azure Storage account
Data Factory stores pipeline-run data for only 45 days. Use Monitor if you want to keep that data for a longer time. With Monitor, you can route diagnostic logs for analysis. You can also keep them in a storage account so that you have factory information for your chosen duration.
Save your diagnostic logs to a storage account for auditing or manual inspection. You can use the diagnostic settings to specify the retention time in days.
References:
https://docs.microsoft.com/en-us/azure/data-factory/concepts-pipeline-execution-triggers
https://docs.microsoft.com/en-us/azure/data-factory/monitor-using-azure-monitor