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NEW QUESTION: 1
Refer to the Exhibit.

An administrator wishes to provide Load Balanced I/O for the device shown in the Exhibit.
To meet this requirement, which setting should be changed?
A. Storage Array Type Policy = VMW_NMP_RR
B. Storage Array Type Policy = VMW_SATP_RR
C. Path Selection Policy = Round Robin (VMware)
D. Path Selection Policy = MRU (VMware)
Answer: C
Explanation:
Explanation/Reference:
Explanation: Round Robin (RR): The VMW_PSP_RR policy uses an automatic path selection, rotating through all available paths, enabling the distribution of load across the configured paths.
For Active/Passive storage arrays, only the paths to the active controller will be used in the Round

Robin policy.
For Active/Active storage arrays, all paths will be used in the Round Robin policy.

Note: For logical units associated with Microsoft Cluster Service (MSCS) and Microsoft Failover Clustering virtual machines, the Round Robin pathing policy is supported only on ESXi 5.5 and later.
Reference: https://kb.vmware.com/selfservice/microsites/search.do?
language=en_US&cmd=displayKC&externalId=1011340

NEW QUESTION: 2
Which of the following is NOT a KPI (key performance indicator) of Agile?
A. Team Attendance
B. Quality Delivered to Customers
C. Actual Stories Completed vs. Committed Stories
D. Team Velocity
E. Technical Debt Management
F. Team Enthusiasm
Answer: A

NEW QUESTION: 3
You manage a process that performs analysis of daily web traffic logs on an HDInsight cluster. Each of 250 web servers generates approximately gigabytes (GB) of log data each day. All log data is stored in a single folder in Microsoft Azure Data Lake Storage Gen 2.
You need to improve the performance of the process.
Which two changes should you make? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
A. Increase the value of the hive.tez.container.size parameter
B. Increase the number of worker nodes
C. Move the log files into folders so that each day's logs are in their own folder
D. Combine the daily log files for all servers into one file
E. Increase the value of the mapreduce.map.memory parameter
Answer: C,D
Explanation:
Explanation
A: Typically, analytics engines such as HDInsight and Azure Data Lake Analytics have a per-file overhead. If you store your data as many small files, this can negatively affect performance. In general, organize your data into larger sized files for better performance (256MB to 100GB in size). Some engines and applications might have trouble efficiently processing files that are greater than 100GB in size.
C: For Hive workloads, partition pruning of time-series data can help some queries read only a subset of the data which improves performance.
Those pipelines that ingest time-series data, often place their files with a very structured naming for files and folders. Below is a very common example we see for data that is structured by date:
\DataSet\YYYY\MM\DD\datafile_YYYY_MM_DD.tsv
Notice that the datetime information appears both as folders and in the filename.
References:
https://docs.microsoft.com/en-us/azure/storage/blobs/data-lake-storage-performance-tuning-guidance