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NEW QUESTION: 1
Identify the three differences between Grid Spreader and Mass Allocate.
A. Grid Spreader processes on the client whereas Mass Allocate processes on the server.
B. Both M-3CG Allocate and Grid Spreader require special roles in Shared Services.
C. Mass Allocate generates and runs a business rule behind the scenes, allowing members not displayed on the form to be updated.
D. Both Mass Allocate and Grid Spreader support relational spread.
E. Grid Spreader gives users a "preview" to the spread result before saving whereas Mass Allocate will automatically save results to the server.
Answer: A,C,E
Explanation:
Explanation/Reference:
Explanation:
Grid spread allocates data across the cells on the webform and runs on the client browser.Mass allocate triggers a calc script which can allocate data beyond cells those are available in the form.
If your administrator has enabled Grid Spread as a data form property, you can specify an amount or percentage by which Planning increases or decreases values across multiple dimensions on the data form, based on the existing values in the target cells. You immediately see the result in the data form and can save the new data or discard it. When calculating the spread data, Planning ignores read-only and locked cells and cells having supporting detail. Data integrity is ensured by spreading values only to cells to which you have access.
If you have the Mass Allocate role (assigned in Oracle's Hyperion® Shared Services) and an administrator has enabled Mass Allocate as the data form property, you can spread data using the powerful feature, Mass Allocate, which:

NEW QUESTION: 2
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: 3
What is a form of cloud storage where data is stored as objects, arranged in a hierarchal structure, like a file tree?
A. Object storage
B. Databases
C. Volume storage
D. Content delivery network (CDN)
Answer: A