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NEW QUESTION: 1
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 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 implement the data partitioning strategy.
How should you partition the Fact.Order table?
A. Use a granularity of two days.
B. Create 730 partitions.
C. Create 17,520 partitions.
D. Create 2,557 partitions.
Answer: D
Explanation:
We create on partition for each day. 7 years times 365 days is 2,555. Make that 2,557 to provide for leap years.
From scenario: Partition the Fact.Order table and retain a total of seven years of data.
Maximize the performance during the data loading process for the Fact.Order partition.

NEW QUESTION: 2
F Company is an international fast food company and is committed to the rational model for strategy.
A senior team has carried out a comprehensive study of the environment in which it currently operates, its resources, distinctive competences and a corporate appraisal. The mission and objectives have been set in accord with stakeholder directives, which are based on twin objectives to grow profits by 5% year on year and become one of the top 100 global brands.
The team has generated a short-list of different strategic initiatives which it believes may deliver the primary objectives required by the stakeholders. Each initiative has been examined separately on its merits in terms of feasibility, suitablity and acceptablity. As many factors as possible have been taken into account such as market analysis, economic growth and competitors.
In terms of the rational model, select the actions that the company has now carried out:
A. Strategy selection / options generation / strategic choice
B. Strategic analysis / options generation / options evaluation
C. Strategy selection / options evaluation / implementation of strategy
D. Strategic analysis / options evaluation / implementation of strategy
Answer: B

NEW QUESTION: 3

A. Option D
B. Option B
C. Option A
D. Option C
Answer: C

NEW QUESTION: 4
A reverse engineer was analyzing malware found on a retailer's network and found code extracting track data in memory.
Which of the following threats did the engineer MOST likely uncover?
A. Key logger
B. Rootkit
C. Ransomware
D. POS malware
Answer: D