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NEW QUESTION: 1
A user is launching an instance. He is on the "Tag the instance" screen. Which of the below mentioned information will not help the user understand the functionality of an AWS tag?
A. AWS tags are used to find the cost distribution of various resources
B. The user can apply tags to the S3 bucket
C. Each tag will have a key and value
D. The maximum value of the tag key length is 64 Unicode characters
Answer: D
Explanation:
Explanation
AWS provides cost allocation tags to categorize and track the AWS costs. When the user applies tags to his AWS resources, AWS generates a cost allocation report as a comma-separated value (CSV file. with the usage and costs aggregated by those tags. Each tag will have a key-value and can be applied to services, such as EC2, S3, RDS, EMR, etc. The maximum size of a tag key is 128 Unicode characters.

NEW QUESTION: 2
Which of the following standards primarily pertains to cabling designs and setups in a data center?
A. NFPA
B. BICSI
C. Uptime Institute
D. IDCA
Answer: B
Explanation:
Explanation
The standards put out by Building Industry Consulting Service International (BICSI) primarily cover complex cabling designs and setups for data centers, but also include specifications on power, energy efficiency, and hot/cold aisle setups.

NEW QUESTION: 3
DRAG DROP
Drag and drop the functions from the left onto the correct network components on the right.

Answer:
Explanation:

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