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NEW QUESTION: 1
What must you set up before you apply the settings for Payroll Posting?
A. Distribution of general ledger
B. Distribution of cost centers
C. Distribution of project
D. Distribution of controlling
Answer: B

NEW QUESTION: 2
Which components are installed with the KPI framework?
There are 2 correct answers to this question. Response:
A. Central UI add-on
B. KPI modeler
C. OData services
D. Generic drill down app
Answer: B,D

NEW QUESTION: 3
Note: This question is part of a series of questions that use the same scenario. For your convenience, the scenario is repeated in each question. Each question presents a different goal and answer choices, but the text of the scenario is exactly the same in each question in this series.
You have a Microsoft SQL Server data warehouse instance that supports several client applications.
The data warehouse includes the following tables: Dimension.SalesTerritory, Dimension.Customer, Dimension.Date, Fact.Ticket, and Fact.Order. The Dimension.SalesTerritory and Dimension.Customer tables are frequently updated. The Fact.Order table is optimized for weekly reporting, but the company wants to change it daily. The Fact.Order table is loaded by using an ETL process. Indexes have been added to the table over time, but the presence of these indexes slows data loading.
All data in the data warehouse is stored on a shared SAN. All tables are in a database named DB1. You have a second database named DB2 that contains copies of production data for a development environment. The data warehouse has grown and the cost of storage has increased. Data older than one year is accessed infrequently and is considered historical.
You have the following requirements:
* Implement table partitioning to improve the manageability of the data warehouse and to avoid the need to repopulate all transactional data each night. Use a partitioning strategy that is as granular as possible.
* - Partition the Fact.Order table and retain a total of seven years of data.
* - Partition the Fact.Ticket table and retain seven years of data. At the end of each month, the partition structure must apply a sliding window strategy to ensure that a new partition is available for the upcoming month, and that the oldest month of data is archived and removed.
* - Optimize data loading for the Dimension.SalesTerritory, Dimension.Customer, and Dimension.Date tables.
* - Incrementally load all tables in the database and ensure that all incremental changes are processed.
* - Maximize the performance during the data loading process for the Fact.Order partition.
* - Ensure that historical data remains online and available for querying.
* - Reduce ongoing storage costs while maintaining query performance for current data.
You are not permitted to make changes to the client applications.
You need to configure the Fact.Order table.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

Answer:
Explanation:

Explanation

From scenario: Partition the Fact.Order table and retain a total of seven years of data. Maximize the performance during the data loading process for the Fact.Order partition.
Step 1: Create a partition function.
Using CREATE PARTITION FUNCTION is the first step in creating a partitioned table or index.
Step 2: Create a partition scheme based on the partition function.
To migrate SQL Server partition definitions to SQL Data Warehouse simply:
Step 3: Execute an ALTER TABLE command to specify the partition function.
References: https://docs.microsoft.com/en-us/azure/sql-data-warehouse/sql-data-warehouse-tables-partition

NEW QUESTION: 4
You have multiple developers working on a terraform project (using terraform OSS), and have saved the terraform state in a remote S3 bucket . However ,team is intermittently experiencing inconsistencies in the provisioned infrastructure / failure in the code . You have traced this problem to simultaneous/concurrent runs of terraform apply command for 2/more developers . What can you do to fix this problem?
A. Stop using remote state , and store the developer tfstate in their own machine . Once a day , all developers should sit together and merge the state files manually , to avoid any inconsistencies.
B. Enable terraform state locking for the S3 backend using DynamoDB table. This prevents others from acquiring the lock and potentially corrupting your state.
C. Use terraform workspaces feature, this will fix this problem by default , as every developer will have their own state file , and terraform will merge them on server side on its own.
D. Structure your team in such a way that only one individual will run terraform apply , everyone will just make changes and share with him. Then there will be no chance of any inconsistencies.
Answer: B
Explanation:
S3 backend support state locking using DynamoDB.
https://www.terraform.io/docs/state/locking.html