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NEW QUESTION: 1
DRAG DROP
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: 2
An Analytics developer placed the doPlugins() function in the code editor of the Adobe Analytics extension to populate the s.campaign variable. Then, the developer created arule to populated the s,compaign variable on a specific landing page.
When validating the value of the s,compaign variable on the specific landing page, it shown the doPlugins() value for the campaign variable.
What would cause this issue?
A. The rules condition was not met. so the doPlugins() function persisted.
B. The doPlugin() function runs last and overwrites any values for the same variables.
C. The doPlugin() function needs to be excluded through an exception in the rule.
D. The doPlugin() function value will take precedence over the s,compaign variable
Answer: D
NEW QUESTION: 3
You are developing a database application by using Microsoft SQL Server 2012.
An application that uses a database begins to run slowly.
You discover that the root cause is a query against a frequently updated table that has a clustered index.
The query returns four columns: three columns in its WHERE clause contained in a non-clustered index and one additional column.
You need to optimize the statement.
What should you do?
A. Add a LOOP hint to the query.
B. Include a SET STATISTICS PROFILE ON statement before you run the query.
C. Include a SET TRANSACTIONISOLATION LEVEL REPEATABLE READ statement before you run the query.
D. Include a SET TRANSACTION ISOLATION LEVEL SERIALIZABLE statement before you run the query.
E. Add a FORCESCAN hint to the Attach query.
F. Add a FORCESEEK hint to the query.
G. Include a SET TRANSACTION ISOLATION LEVEL SNAPSHOT statement before you run the query.
H. Cover the unique clustered index with a columnstore index.
I. Enable the optimize for ad hoc workloads option.
J. Add a HASH hint to the query.
K. Add an INCLUDE clause to the index.
L. Include a SET FORCEPLAN ON statement before you run the query.
M. Add a columnstore index to cover the query.
N. Include a SET STATISTICS SHOWPLAN_XML ON statement before you run the query.
Answer: F
Explanation:
Explanation/Reference:
Explanation: