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NEW QUESTION: 1
Refer to the exhibit. This exhibit shows the NAT configuration for Router A and the output for a ping issued from device 171.68.200.48 and destined to 172.16.47.142. Based on this information, what change must be made to Router A in order for the ping to work?

A. reload the router
B. add a static route
C. clear the route cache
D. load a newer IOS image
E. configure IP as classless
Answer: E
Explanation:
The assumption with classful routing is that all of a classful address belongs to one organization. The idea of a component subnet would be any subnet of a classful network. When you look at the routing table it lists the subnets based on the classful networks like this: Network 192.168.1.0/27 is subnetted 2 subnets 192.168.1.32 .... 192.168.1.64 .... The 192.168.1.32 and 192.168.1.64 subnets would be the "component subnets" of the 192.168.1.0 network. So the point is that if you tried to route a packet to 192.168.1.151 it would be dropped even if there was a default route in the routing table. The reason fo this is that the router knows of subnets that belong to the major network (192.168.1.0 in this case), but does not have an entry for the specific subnet.

NEW QUESTION: 2
展示を参照してください。

ルータR1は3つの異なるルーティングプロトコルを実行しています。ルーターが宛先IP 172.16.32.1で受信したパケットを転送するために使用するルート特性はどれですか。
A. 管理距離
B. メトリック
C. コスト
D. 最長の接頭辞
Answer: D

NEW QUESTION: 3
Which piece of information is part of the WBS Dictionary?
A. Organizational process assets
B. Change requests
C. Validated deliverables
D. Responsible organization
Answer: D

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