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Question 201

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.

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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 SQL.

✑ 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 High Concurrency cluster for the data engineers, and a Standard cluster for the jobs.

Does this meet the goal?

    Correct Answer: A

    To meet the requirements, we need a High Concurrency cluster for the data engineers to allow efficient resource sharing. Data scientists require individual Standard clusters with auto-termination to support their ad hoc analysis. For the job workloads, using Standard clusters is appropriate as it supports running packages developed in Python, Scala, and SQL, ensuring compatibility with the provided languages. Therefore, setting up a Standard cluster for each data scientist, a High Concurrency cluster for data engineers, and a Standard cluster for jobs satisfactorily meets the stated goals.

Discussion
AmalbenrebaiOption: B

- data engineers: high concurrency cluster - jobs: Standard cluster - data scientists: Standard cluster

Julius7000

Tell me one thing: is this answer 9jobs) based on the text: "A Single Node cluster has no workers and runs Spark jobs on the driver node. In contrast, a Standard cluster requires at least one Spark worker node in addition to the driver node to execute Spark jobs."? I dont understand the connection between worker noodes and the requirements given in the question about jobs workspace.

Aditya0891

single node cluster and standard cluster are different. In single node cluster you only have 1 node which act as driver and worker node while in standard cluster you can have separate driver and worker node and for jobs you can use standard or high concurrency cluster as well. So the requirements are satisfied here

Egocentric

agreed

supriyako

Correct. Because jobs could be for Scala notebook, which is supported by Standard cluster mode

gogosgh

The issue is the jobs are going to be ran by multiple users i.e. engineers and scientists? So it needs to be hugi concurrency cluster?

auwia

If you enable high concurrency then all scale scripts doesn't works, so scientists will stop to work). Standard cluster is scalable, will support all jobs and users! ;-)

gangstfearOption: A

The answer must be A!

HanseOption: B

As per Link: https://docs.azuredatabricks.net/clusters/configure.html Standard and Single Node clusters terminate automatically after 120 minutes by default. --> Data Scientists High Concurrency clusters do not terminate automatically by default. A Standard cluster is recommended for a single user. --> Standard for Data Scientists & High Concurrency for Data Engineers Standard clusters can run workloads developed in any language: Python, SQL, R, and Scala. High Concurrency clusters can run workloads developed in 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. --> Jobs needs Standard

Ast999Option: A

SCALA = STANDARD

auwiaOption: A

High concurrence doesn't support scala.

Eyepatch993Option: B

Standard clusters do not have fault tolerance. Both the data scientist and data engineers will be using the job cluster for processing their notebooks, so if a standard cluster is chosen and a fault occurs in the notebook of any one user, there is a chance that other notebooks might also fail. Due to this a high concurrency cluster is recommended for running jobs.

Boompiee

It may not be a best practice, but the question asked is: does the solution meet the stated requirements, and it does..

Aditya0891

Read the question properly. it states that each data scientist will have a standard cluster and a separate standard cluster for running jobs. So there is no question of fault due to other users. The answer is A

allagowfOption: A

data scientists and Job --> Scala --> Standard cluster .

dakku987Option: B

we need HC cluster for data engineer,data scientist,jobs

sethuramanspOption: B

The answer should be "NO" as per the given statement "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." since the Job cluster is standard it will not allow data scientists and engineers to collectively deploy their Notebooks in standard cluster as it requires High Concurrency Cluster

wanchihh

High Concurrency Cluster does not support Scala.

AccountHatz

The Shared access mode clusters aka the former High Concurrency cluster do now support Scala , they do not support R (https://learn.microsoft.com/en-us/azure/databricks/archive/compute/cluster-ui-preview)

mav2000

Exactly! before, the cluster should've been Standard, because it wasn't able to support Scala, but now that it can, the best cluster is High concurrency from all the people executing jobs there.

ovokpusOption: A

Yes it seems to be!

PallaviPatelOption: A

correct

DanweoOption: A

A is correct

kkk5566Option: A

correct - data engineers: high concurrency cluster - jobs: Standard cluster - data scientists: Standard cluster

auwiaOption: A

True, correct.

greenleverOption: A

Correct

anks84Option: A

We would need a Standard cluster for the jobs to support Scala. High-concurrecny cluster does not support Scala. Hence, the Answer is A !

Deeksha1234Option: B

the answer should be No

Deeksha1234

sorry 'A' should be correct