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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 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 implement the data partitioning strategy.
How should you partition the Fact.Order table?
A. Create 17,520 partitions.
B. Use a granularity of two days.
C. Create 730 partitions.
D. Create 2,557 partitions.
Answer: D
Explanation:
Explanation/Reference:
Explanation:
We create on partition for each day, which means that a granularity of one day is used. 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.
The Fact.Order table is optimized for weekly reporting, but the company wants to change it to daily.
Maximize the performance during the data loading process for the Fact.Order partition.
Reference: https://docs.microsoft.com/en-us/azure/sql-data-warehouse/sql-data-warehouse-tables- partition

NEW QUESTION: 2
After a Report Generation job has completed successfully, what is the first step to change the report schedule?
A. select the report template in My Reports View
B. edit the Report job
C. start the Schedule Report Wizard
D. start the Report Customization Wizard
Answer: B

NEW QUESTION: 3
Intel@ Raid products are categorized as either: Entry, Mainstream or Performance. Which of the following statements regarding the feature differences between these categories are true? (Select ALL that apply).
A. Intel Entry RAID products do not support RAID levels 6 or 60.
B. All categories of Intel@ Raid products support battery backup options.
C. All categories of Intel@ Raid products support a Global Hot Spare drive.
D. Only Mainstream and Performance categories support Full Disk Encryption.
E. Intel Mainstream RAID products support a maximum of 240 physical disks.
Answer: A,C,D

NEW QUESTION: 4
会社は、ウェブサイトでアイテムの検索可能なリポジトリを維持しています。Tieデータは、1,000万行を超えるAmazon RDS for MySQLデータベーステーブルに保存されています。データベースには、2 TBの汎用SSD（gp2）ストレージがあります。同社のWebサイトでは、このデータに対して毎日何百万もの更新が行われています。一部の操作に10秒以上かかることに気付き、データベースストレージのパフォーマンスがボトルネックであると判断しました。パフォーマンスの問題に対処するソリューションはどれですか。
A. MySQLネイティブ非同期レプリケーションでマルチAZ RDSリードレプリカを有効にする
B. インスタンスをメモリ最適化インスタンスクラスに変更します
C. インスタンスをバースト可能なパフォーマンスDBインスタンスクラスに変更します
D. ストレージタイプをプロビジョンドIOPS SSD（io1）に変更します
Answer: B