Microsoft Certified Azure Data Engineer Associate · Free Practice Question Hard
Question 13
Scenario: You are working as a consultant at Advanced Idea Mechanics (A.I.M.) who is a privately funded think tank organized of a group of brilliant scientists whose sole dedication is to acquire and develop power through technological means. Their goal is to use this power to overthrow the governments of the world. They supply arms and technology to radicals and subversive organizations in order to foster a violent technological revolution of society while making a profit.
The company has 10,000 employees. Most employees are located in Europe. The company supports teams worldwide.
AIM has two main locations: a main office in London, England, and a manufacturing plant in Berlin, Germany.
At the moment, you are leading a Workgroup meeting with the IT Team where the topic of discussion is Azure Databricks.
The IT team plans 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 AIM 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.
Required: Create the Databricks clusters for the workloads.
Proposed Solution: The team decides to create a High Concurrency cluster for each data scientist, a High Concurrency cluster for the data engineers, and a Standard cluster for the jobs.
Does this meet the requirement?
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A
Yes
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B
No
Reveal correct answer
Correct answer: B
Explanation
High-concurrency clusters do not support Scala.
Standard clusters
Standard clusters are recommended for a single user. Standard clusters can run workloads developed in any language: Python, R, Scala, and SQL.
High Concurrency clusters
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.
High Concurrency clusters work only for SQL, Python, and R. The performance and security of High Concurrency clusters is provided by running user code in separate processes, which is not possible in Scala.
In addition, only High Concurrency clusters support table access control.
A.
No, the solution does not meet the requirement. The enterprise architecture team at AIM specifies that all data scientists must be assigned their own cluster that terminates automatically after 120 minutes of inactivity. However, the solution suggests creating a High Concurrency cluster for each data scientist, which does not align with the requirement of assigning individual clusters to data scientists.
B. No, the solution does not meet the requirement. The enterprise architecture team at AIM specifies that all data scientists must be assigned their own cluster that terminates automatically after 120 minutes of inactivity. However, the solution suggests creating a High Concurrency cluster for each data scientist, which does not align with the requirement of assigning individual clusters to data scientists.
Discussion
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