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NEW QUESTION: 1
Which two workload metrics are used to calculate replication bandwidth requirements? (Choose two.)
A. total MB/sec throughput
B. disk latency
C. host response time
D. read/write ratio
Answer: A,D

NEW QUESTION: 2

A. Option A
B. Option B
C. Option D
D. Option C
Answer: B

NEW QUESTION: 3
Why should the analyst place special emphasis in their assessment of cash balances?
A. Cash is a company's most liquid asset.
B. All of these answers.
C. None of these answers.
D. Cash represents the point in a firm's operating cycle where management has maximum discretion with the deployment and use of its resources.
E. Cash is typically the only asset that is available to meet a firm's financial obligations.
Answer: B
Explanation:
Explanation/Reference:
Explanation:
All of these answers are correct because any assessment of a firm's liquidity should be focused on a firm's ability to meet its near-term liabilities. While inventory may be sold, it is subject to shrinkage through loss of value, theft, etc. and while accounts receivable a current assets, not all firms collect the full balance of what they are owed, let alone collecting it when it is due. Cash is king in the world of finance because it is cash and cash alone that is used to meet financial commitments.

NEW QUESTION: 4
You develop data engineering solutions for a company.
A project requires an in-memory batch data processing solution.
You need to provision an HDInsight cluster for batch processing of data on Microsoft Azure.
How should you complete the PowerShell segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation:
Box 1: New-AzStorageContainer
# Example: Create a blob container. This holds the default data store for the cluster.
New-AzStorageContainer `
-Name $clusterName `
-Context $defaultStorageContext
$sparkConfig = New-Object "System.Collections.Generic.Dictionary``2[System.String,System.String]"
$sparkConfig.Add("spark", "2.3")
Box 2: Spark
Spark provides primitives for in-memory cluster computing. A Spark job can load and cache data into memory and query it repeatedly. In-memory computing is much faster than disk-based applications than disk-based applications, such as Hadoop, which shares data through Hadoop distributed file system (HDFS).
Box 3: New-AzureRMHDInsightCluster
# Create the HDInsight cluster. Example:
New-AzHDInsightCluster `
-ResourceGroupName $resourceGroupName `
-ClusterName $clusterName `
-Location $location `
-ClusterSizeInNodes $clusterSizeInNodes `
-ClusterType $"Spark" `
-OSType "Linux" `
Box 4: Spark
HDInsight is a managed Hadoop service. Use it deploy and manage Hadoop clusters in Azure. For batch processing, you can use Spark, Hive, Hive LLAP, MapReduce.
References:
https://docs.microsoft.com/bs-latn-ba/azure/hdinsight/spark/apache-spark-jupyter-spark-sql-use-powershell
https://docs.microsoft.com/bs-latn-ba/azure/hdinsight/spark/apache-spark-overview