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Download Data Engineering on Microsoft Azure Exam Dumps
NEW QUESTION 37
The storage account container view is shown in the Refdata exhibit. (Click the Refdata tab.)
You need to configure the Stream Analytics job to pick up the new reference data.
What should you configure?
To answer, select the appropriate options in the answer area
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION 38
You have an enterprise data warehouse in Azure Synapse Analytics named DW1 on a server named Server1.
You need to verify whether the size of the transaction log file for each distribution of DW1 is smaller than
160 GB.
What should you do?
- A. From Azure Monitor in the Azure portal, execute a query against the logs of DW1.
- B. Execute a query against the logs of DW1 by using the Get-AzOperationalInsightSearchResult PowerShell cmdlet.
- C. On DW1, execute a query against the sys.database_files dynamic management view.
- D. On the master database, execute a query against the sys.dm_pdw_nodes_os_performance_counters dynamic management view.
Answer: D
Explanation:
Explanation
The following query returns the transaction log size on each distribution. If one of the log files is reaching
160 GB, you should consider scaling up your instance or limiting your transaction size.
-- Transaction log size
SELECT
instance_name as distribution_db,
cntr_value*1.0/1048576 as log_file_size_used_GB,
pdw_node_id
FROM sys.dm_pdw_nodes_os_performance_counters
WHERE
instance_name like 'Distribution_%'
AND counter_name = 'Log File(s) Used Size (KB)'
References:
https://docs.microsoft.com/en-us/azure/sql-data-warehouse/sql-data-warehouse-manage-monitor
NEW QUESTION 39
You need to design a data retention solution for the Twitter feed data records. The solution must meet the customer sentiment analytics requirements.
Which Azure Storage functionality should you include in the solution?
- A. soft delete
- B. time-based retention
- C. lifecycle management
- D. change feed
Answer: C
Explanation:
Explanation
Scenario: Purge Twitter feed data records that are older than two years.
Data sets have unique lifecycles. Early in the lifecycle, people access some data often. But the need for access often drops drastically as the data ages. Some data remains idle in the cloud and is rarely accessed once stored.
Some data sets expire days or months after creation, while other data sets are actively read and modified throughout their lifetimes. Azure Storage lifecycle management offers a rule-based policy that you can use to transition blob data to the appropriate access tiers or to expire data at the end of the data lifecycle.
Reference:
https://docs.microsoft.com/en-us/azure/storage/blobs/lifecycle-management-overview
NEW QUESTION 40
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