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NEW QUESTION: 1
In HTML5, the autofocus attribute:
A. defines the cursor location when a form is first accessed.
B. enables one field and disables all others.
C. controls the movement between fields in a form.
D. adds a default value to the field in a form.
Answer: A

NEW QUESTION: 2
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 optimize the storage for the data warehouse.
What change should you make?
A. Implement row compression for the Fact.Order table.
B. Create new tables on lower-cost storage, move the historical data to the new tables, and then shrink the database.
C. Move historical data to new tables on lower-cost storage.
D. Move the index for the Fact.Order table to lower-cost storage.
E. Remove the historical data from the database to leave available space for new data.
F. Partition the Fact.Order table, and move historical data to new filegroups on lower-cost storage.
Answer: F
Explanation:
Explanation/Reference:
Explanation:
Create the load staging table in the same filegroup as the partition you are loading.
Create the unload staging table in the same filegroup as the partition you are deleting.
From scenario: The data warehouse has grown and the cost of storage has increased. Data older than one year is accessed infrequently and is considered historical.
References: https://blogs.msdn.microsoft.com/sqlcat/2013/09/16/top-10-best-practices-for-building-a-large- scale-relational-data-warehouse/

NEW QUESTION: 3
You have successfully gained access to a linux server and would like to ensure that the succeeding outgoing traffic from this server will not be caught by a Network Based Intrusion Detection Systems (NIDS).
What is the best way to evade the NIDS?
A. Protocol Isolation
B. Out of band signalling
C. Alternate Data Streams
D. Encryption
Answer: D
Explanation:
When the NIDS encounters encrypted traffic, the only analysis it can perform is packet level analysis, since the application layer contents are inaccessible. Given that exploits against today's networks are primarily targeted against network services (application layer entities), packet level analysis ends up doing very little to protect our core business assets.
References: http://www.techrepublic.com/article/avoid-these-five-common-ids- implementation-errors/