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NEW QUESTION: 1
Which of the following memory types is unique in that it generates so much heat it requires that the memory modules are covered in heat sinks?
A. DDR
B. RAMBUS
C. SDRAM
D. DDR3
Answer: B
NEW QUESTION: 2
An attacker with access to the inside network of a small company launches a successful STP manipulation
attack. What will he do next?
A. He will repeat the same attack against all L2 switches of the network.
B. He will create a SPAN entry on the spoofed root bridge and redirect traffic to his computer.
C. He will activate OSPF on the spoofed root bridge.
D. He will repeat this action so that is escalates to a DoS attack.
Answer: B
NEW QUESTION: 3
Refer to the exhibit. Which two statements about Cisco Prime Infrastructure are true? (Choose two.)
A. Planning mode requires a special license in Cisco Prime Infrastructure.
B. It shows the planning mode feature in Cisco Prime Infrastructure.
C. It presents the recommended number of APs for the selected coverage area based on the selections made.
D. Controllers must be synchronized with Cisco Prime Infrastructure for planning mode to work.
E. It shows the map editor feature in Cisco Prime Infrastructure.
Answer: B,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 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 to 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 data loading for the tables.
Which data loading technology should you use for each table? To answer, select the appropriate options in the answer area.
Answer:
Explanation:
Explanation
Scenario: The Dimension.SalesTerritory and Dimension.Customer tables are frequently updated Optimize data loading for the Dimension.SalesTerritory, Dimension.Customer, and Dimension.Date tables.
Box 1: Change Tracking
Box 2: Change Tracking
Box 3: Temporal Table
Temporal Tables are generally useful in scenarios that require tracking history of data changes.
We recommend you to consider Temporal Tables in the following use cases for major productivity benefits.
* Slowly-Changing Dimensions
Dimensions in data warehousing typically contain relatively static data about entities such as geographical locations, customers, or products.
References:
https://docs.microsoft.com/en-us/sql/relational-databases/tables/temporal-table-usage-scenarios