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NEW QUESTION: 1
Scenario:
CGE is experiencing numerous issues with its existing profile management solution. The Citrix Architect managing the project is tasked with designing a solution that alleviates existing issues and, if possible, minimizes the loss of existing customizations. Configuration changes need to be implemented quickly and with minimal complexity.
How can Citrix Profile management be configured for Sales end-user group profiles?
A. Enable file and folder exclusions
B. Enable 'Profile streaming'
C. Enable 'Active write back'
D. Enable 'Offline profile support'
Answer: A

NEW QUESTION: 2
About Thin LUN and Thick LUN of OceaStor V3 storage, which are the correct statements in the following? (Multiple Choice)
A. Both ThinLUN and Thick LUN can do the initial capacity allocation
B. Both ThinLUN and Thick LUN's maximum capacity can be greater than or equal the available capacity of the storage pool where they in
C. Both ThinLUN and Thick LUN can be created in bulk
D. ThinLUN and Thick LUN can configure the same capacity.
Answer: C,D

NEW QUESTION: 3
注：この質問は、同じシナリオを提示する一連の質問の一部です。 シリーズの各質問には、記載された目標を達成する可能性のある独自のソリューションが含まれています。 一部の質問セットには複数の正しい解決策がある場合もあれば、正しい解決策がない場合もあります。
このシナリオで質問に答えた後、その質問に戻ることはできません。 その結果、これらの質問はレビュー画面に表示されません。
階層構造を持つAzure Databricksワークスペースを作成する予定です。 ワークスペースには、次の3つのワークロードが含まれます。
* A workload for data engineers who will use Python and SQL
* A workload for jobs that will run notebooks that use Python, Spark, 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 Databrick clusters for the workloads.
Solution: You create a Standard cluster for each data scientist, a High Concurrency cluster for the data engineers, and a High Concurrency cluster for the jobs.
Does this meet the goal?
A. No
B. Yes
Answer: B
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.
References:
https://docs.azuredatabricks.net/clusters/configure.html