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NEW QUESTION: 1
A. System Configuration
B. Storage Explorer
C. Server Manager
D. Shared Folders
E. Disk Management
F. File Server Resource Manager (FSRM)
G. Computer Management
H. File Explorer
Answer: H
NEW QUESTION: 2
A customer plans to use Apollo 6000 chassis for scalability and efficiency. The customer will deploy an application cluster that requires two I/O accelerator cards per server for optimum performance.
Which server should the architect recommend to the customer?
A. DL380 Gen9
B. XL250aGen9 server tray
C. XL220a Gen8 v2 server tray
D. XL230a Gen9 server tray
Answer: B
Explanation:
Explanation/Reference:
Explanation:
The XL250a Gen9 tray supports two I/O accelerator cards per server for top notch performance. It is the best in the industry and the best configuration available for Apollo 6000 chassis.
Reference:https://www.hpe.com/h20195/v2/getpdf.aspx/c04293373.pdf?ver=9
NEW QUESTION: 3
What should you do so that Oracle IT Analytics analyzes specific targets from Oracle Enterprise Manager?
A. Within the Oracle IT Analytics display, choose Group and then create a system "ITA group" and add the targets to it.
B. Create a group "omcgroup" in Oracle Enterprise Manager Cloud Control and add chosen target to the group.
C. Within the Oracle Cloud My Services target, select the "selective targets" check box.
D. Configure the Gateway to filter out any targets that Oracle IT Analytics should not analyze.
Answer: A
NEW QUESTION: 4
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 the series.
Start of repeated scenario
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 to daily. The FactOrder 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.
* 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 FactOrder 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.
End of repeated scenario
You need to optimize data loading for the Dimension.SalesTerritory, Dimension.Customer, and Dimension.Date tables.
Which technology should you use for each table?
To answer, select the appropriate technologies in the answer area.
Answer:
Explanation:
Explanation
Box 1: Temporal table
Box 2: Temporal table
Compared to CDC, Temporal tables are more efficient in storing historical data as it ignores insert actions.
Box 3: Change Data Capture (CDC)
By using change data capture, you can track changes that have occurred over time to your table. This kind of functionality is useful for applications, like a data warehouse load process that need to identify changes, so they can correctly apply updates to track historical changes over time.
CDC is good for maintaining slowly changing dimensions.
Scenario: Optimize data loading for the Dimension.SalesTerritory, Dimension.Customer, and Dimension.Date tables.
The Dimension.SalesTerritory and Dimension.Customer tables are frequently updated.
References:
https://www.mssqltips.com/sqlservertip/5212/sql-server-temporal-tables-vs-change-data-capture-vs-change-track
https://docs.microsoft.com/en-us/sql/relational-databases/tables/temporal-table-usage-scenarios?view=sql-server