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NEW QUESTION: 1
DRAG DROP
A user is unable to connect to the Internet. Based on the layered approach to troubleshooting and beginning with the lowest layer, drag each procedure on the left to its proper category on the right.

Answer:
Explanation:

Explanation:

The question asks us to "begin with the lowest layer" so we have to begin with Layer 1:
verify physical connection; in this case an Ethernet cable connection. For your information,
"verify Ethernet cable connection" means that we check if the type of connection
(crossover, straight-through, rollover...) is correct, the RJ45 headers are plugged in, the
signal on the cable is acceptable...
Next we "verify NIC operation". We do this by simply making a ping to the loopback
interface 127.0.0.1. If it works then the NIC card (layer 1, 2) and TCP/IP stack (layer 3) are
working properly.
Verify IP configuration belongs to layer 3. For example, checking if the IP can be
assignable for host, the PC's IP is in the same network with the gateway...
Verifying the URL by typing in your browser some popular websites like google.com,
microsoft.com to assure that the far end server is not down (it sometimes make we think
we can't access to the Internet). We are using a URL so this step belongs to layer 7 of the
OSI model.

NEW QUESTION: 2
Processes having the "CAP_NET_BIND_SERVICE" can listen on which ports?
A. Any TCP port under 1024
B. Any TCP port over 1024
C. Any UDP port over 1024
D. Any UDP port under 1024
Answer: A

NEW QUESTION: 3
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