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NEW QUESTION # 36

Note: This question is part of a series of questions that present the same scenario. Each question in 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 plan to create an Azure Databricks workspace that has a tiered structure. The workspace will contain the following three workloads:

* A workload for data engineers who will use Python and SQL.

* A workload for jobs that will run notebooks that use Python, Scala, and SOL.

* A workload that data scientists will use to perform ad hoc analysis in Scala and R.

The enterprise architecture team at your company identifies the following standards for Databricks environments:

* The data engineers must share a cluster.

* The job cluster will be managed by using a request process whereby data scientists and data engineers provide packaged notebooks for deployment to the cluster.

* All the data scientists must be assigned their own cluster that terminates automatically after 120 minutes of inactivity. Currently, there are three data scientists.

You need to create the Databricks clusters for the workloads.

Solution: You create a Standard cluster for each data scientist, a Standard cluster for the data engineers, and a High Concurrency cluster for the jobs.

Does this meet the goal?

  • A. No
  • B. Yes

Answer: A

Explanation:

Explanation

We need a High Concurrency cluster for the data engineers and the jobs.

Note: Standard clusters are recommended for a single user. Standard can run workloads developed in any language: Python, R, Scala, and SQL.

A high concurrency cluster is a managed cloud resource. The key benefits of high concurrency clusters are that they provide Apache Spark-native fine-grained sharing for maximum resource utilization and minimum query latencies.

Reference:

https://docs.azuredatabricks.net/clusters/configure.html



NEW QUESTION # 37

You are processing streaming data from vehicles that pass through a toll booth.

You need to use Azure Stream Analytics to return the license plate, vehicle make, and hour the last vehicle passed during each 10-minute window.

How should you complete the query? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.

Answer:

Explanation:



Explanation



Graphical user interface, text, application Description automatically generated



Box 1: MAX

The first step on the query finds the maximum time stamp in 10-minute windows, that is the time stamp of the last event for that window. The second step joins the results of the first query with the original stream to find the event that match the last time stamps in each window.

Query:

WITH LastInWindow AS

(

SELECT

MAX(Time) AS LastEventTime

FROM

Input TIMESTAMP BY Time

GROUP BY

TumblingWindow(minute, 10)

)

SELECT

Input.License_plate,

Input.Make,

Input.Time

FROM

Input TIMESTAMP BY Time

INNER JOIN LastInWindow

ON DATEDIFF(minute, Input, LastInWindow) BETWEEN 0 AND 10

AND Input.Time = LastInWindow.LastEventTime

Box 2: TumblingWindow

Tumbling windows are a series of fixed-sized, non-overlapping and contiguous time intervals.

Box 3: DATEDIFF

DATEDIFF is a date-specific function that compares and returns the time difference between two DateTime fields, for more information, refer to date functions.

Reference:

https://docs.microsoft.com/en-us/stream-analytics-query/tumbling-window-azure-stream-analytics



NEW QUESTION # 38

You are processing streaming data from vehicles that pass through a toll booth.

You need to use Azure Stream Analytics to return the license plate, vehicle make, and hour the last vehicle passed during each 10-minute window.

How should you complete the query? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.

Answer:

Explanation:



Reference:

https://docs.microsoft.com/en-us/stream-analytics-query/tumbling-window-azure-stream-analytics



NEW QUESTION # 39

You need to create an Azure Data Factory pipeline to process data for the following three departments at your company: Ecommerce, retail, and wholesale. The solution must ensure that data can also be processed for the entire company.

How should you complete the Data Factory data flow script? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.

NOTE: Each correct selection is worth one point.

Answer:

Explanation:



Reference:

https://docs.microsoft.com/en-us/azure/data-factory/data-flow-conditional-split



NEW QUESTION # 40

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