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NEW QUESTION: 1
What are two methods that can be used to denormalize tables in BigQuery?
A. 1) Use nested repeated fields; 2) Use a partitioned table
B. 1) Join tables into one table; 2) Use nested repeated fields
C. 1) Use a partitioned table; 2) Join tables into one table
D. 1) Split table into multiple tables; 2) Use a partitioned table
The conventional method of denormalizing data involves simply writing a fact, along with all its dimensions, into a flat table structure. For example, if you are dealing with sales transactions, you would write each individual fact to a record, along with the accompanying dimensions such as order and customer information.
The other method for denormalizing data takes advantage of BigQuery's native support for nested and repeated structures in JSON or Avro input data. Expressing records using nested and repeated structures can provide a more natural representation of the underlying data. In the case of the sales order, the outer part of a JSON structure would contain the order and customer information, and the inner part of the structure would contain the individual line items of the order, which would be represented as nested, repeated elements.
NEW QUESTION: 2
Contoso, Ltd., is the developer of an enterprise resource planning (ERP) application.
Contoso is designing a new version of the ERP application. The previous version of the ERP application
used SQL Server 2008 R2.
The new version will use SQL Server 2014.
The ERP application relies on an import process to load supplier data. The import process updates
thousands of rows simultaneously, requires exclusive access to the database, and runs daily.
You receive several support calls reporting unexpected behavior in the ERP application. After analyzing
the calls, you conclude that users made changes directly to the tables in the database.
The current database schema contains a table named OrderDetails.
The OrderDetails table contains information about the items sold for each purchase order. OrderDetails
stores the product ID, quantities, and discounts applied to each product in a purchase order.
The product price is stored in a table named Products. The Products table was defined by using the
A column named ProductName was created by using the varchar data type. The database contains a
table named Orders.
Orders contains all of the purchase orders from the last 12 months. Purchase orders that are older than
12 months are stored in a table named OrdersOld.
The previous version of the ERP application relied on table-level security.
The current version of the database contains stored procedures that change two tables. The following
shows the relevant portions of the two stored procedures:
The current version of the ERP application requires that several SQL Server logins be set up to function
correctly. Most customers set up the ERP application in multiple locations and must create logins multiple
Index Fragmentation Issues
Customers discover that clustered indexes often are fragmented. To resolve this issue, the customers
defragment the indexes more frequently. All of the tables affected by fragmentation have the following
columns that are used as the clustered index key:
Customers who have large amounts of historical purchase order data report that backup time is
Users report that when they search product names, the search results exclude product names that contain
accents, unless the search string includes the accent.
Missing Data Issues
Customers report that when they make a price change in the Products table, they cannot retrieve the price
that the item was sold for in previous orders.
Query Performance Issues
Customers report that query performance degrades very quickly. Additionally, the customers report that
users cannot run queries when SQL Server runs maintenance tasks. Import Issues During the monthly
import process, database administrators receive many supports call from users who report that they
cannot access the supplier data. The database administrators want to reduce the amount of time required
to import the data.
File Storage Requirements
The ERP database stores scanned documents that are larger than 2 MB. These files must only be
accessed through the ERP application. File access must have the best possible read and write
Data Recovery Requirements
If the import process fails, the database must be returned to its prior state immediately.
You must provide users with the ability to execute functions within the ERP application, without having
direct access to the underlying tables.
You must reduce the likelihood of deadlocks occurring when Sales.Prod and Sales.Proc2 execute.
You need to recommend a solution that addresses the file storage requirements.
What should you include in the recommendation?
A. The varbinary data type
C. The image data type
- Scenario: File Storage Requirements The ERP database stores scanned documents that are larger than
2 MB. These files must only be accessed through the ERP application. File access must have the best
possible read and write performance.
- FileTables remove a significant barrier to the use of SQL Server for the storage and management of
unstructured data that is currently residing as files on file servers.
Enterprises can move this data from file servers into FileTables to take advantage of integrated
administration and services provided by SQL Server. At the same time, they can maintain Windows
application compatibility for their existing Windows applications that see this data as files in the file system.