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NEW QUESTION: 1
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 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 goal?
A. No
B. Yes
Answer: A
Explanation:
Need a High Concurrency cluster for the jobs.
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: 2
Examine this command:

Which statement is true?
A. Four worker processes are allocated to export data and metadata
B. Four worker processes are allocated to export only data
C. Up to four worker processes are allocated to export data and metadata
D. Four worker processes are active to export only metadata
E. Four worker processes are allocated when there is sufficient CPU available
Answer: A
Explanation:
Explanation/Reference:
Reference: https://oracle-base.com/articles/10g/oracle-data-pump-10g

NEW QUESTION: 3
프로젝트를 실행하는 동안 한 고객 담당자가 프로젝트 팀과 다른 요구 사항을 이해하기 때문에 문제가 발생합니다. 고객 담당자는 프로젝트의이 부분에 대한 승인을 거부합니다. 프로젝트 관리자는 어떻게 해야 합니까?
A. 문제를 고객의 프로젝트 관리자에게 이관하여 상황을 해결하십시오.
B. 투명성을 유지하기 위해 통신 관리 계획을 업데이트
C. 위험 레지스터를 업데이트하기 위해 변경 요청을 발행
D. 차이점을 해결하기 위해 충돌 해결 기술을 선택하십시오.
Answer: D

NEW QUESTION: 4
You need to visually identify whether outliers exist in the Age column and quantify the outliers before the outliers are removed.
Which three Azure Machine Learning Studio modules should you use in sequence? To answer, move the appropriate modules from the list of modules to the answer area and arrange them in the correct order.

Answer:
Explanation:

Explanation
Create Scatterplot
Summarize Data
Clip Values
You can use the Clip Values module in Azure Machine Learning Studio, to identify and optionally replace data values that are above or below a specified threshold. This is useful when you want to remove outliers or replace them with a mean, a constant, or other substitute value.
References:
https://blogs.msdn.microsoft.com/azuredev/2017/05/27/data-cleansing-tools-in-azure-machine-learning/
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/clip-values

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