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NEW QUESTION: 1
In WebLogic 10.3.6 and 12c, transaction logs can optionally write to a JDBC store instead of a file store on the file system. Identify the three benefits as a result of this capability.
A. better performance than writing logs to a file store
B. common replication of transaction logs with application data
C. simplified disaster recovery architecture and efforts
D. common storage of transaction logs with application data
E. higher transaction throughput
Answer: B,C,D
Explanation:
Explanation/Reference:
Comparing File Stores and JDBC-accessible Stores
The following are some similarities and differences between file stores and JDBC-accessible stores:
* (A) JDBC stores may make it easier to handle failure recovery since the JDBC interface can access the database from any machine on the same network. With the file store, the disk must be shared or migrated.
* The default persistent store can only be a file store. Therefore, a JDBC store cannot be used as a default persistent store.
* Both have the same transaction semantics and guarantees. As with JDBC store writes, file store writes are guaranteed to be persisted to disk and are not simply left in an intermediate (that is, unsafe) cache.
* Both have the same application interface (no difference in application code).
* (not B, not E) All things being equal, file stores generally offer better throughput than a JDBC store.
/If a database is running on high-end hardware with very fast disks, and WebLogic Server is running on slower hardware or with slower disks, then you may get better performance from the JDBC store.
* File stores are generally easier to configure and administer, and do not require that WebLogic subsystems depend on any external component.
* File stores generate no network traffic; whereas, JDBC stores generate network traffic if the database is on a different machine from WebLogic Server.
Reference: Configuring Server Environments for Oracle WebLogic Server 12c, Comparing File Stores and JDBC-accessible Stores

NEW QUESTION: 2
Overview
Application Overview
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.
Tables
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 SQL_Latin1_General_CP1_CI_AS collation.
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.
Stored Procedures
The current version of the database contains stored procedures that change two tables. The following shows the relevant portions of the two stored procedures:

Customer Problems
Installation Issues
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 times.
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:

Backup Issues
Customers who have large amounts of historical purchase order data report that backup time is unacceptable.
Search Issues
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.
Design Requirements
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.
Data Recovery Requirements
If the import process fails, the database must be returned to its prior state immediately.
Security Requirements
You must provide users with the ability to execute functions within the ERP application, without having direct access to the underlying tables.
Concurrency Requirements
You must reduce the likelihood of deadlocks occurring when Sales.Prod and Sales.Proc2 execute.
You need to recommend which statement should be used to update SalesOrder.
How should you recommend completing the statement? To answer, drag the appropriate elements to the correct locations. Each element may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.

Answer:
Explanation:

Explanation

NEW QUESTION: 3
Scale1という名前の仮想マシンスケールセットを作成します。 Scale1は、次の図に示すように構成されています。

ドロップダウンメニューを使用して、図に示されている情報に基づいて各ステートメントを完了する回答を選択します。

Answer:
Explanation:

Explanation:
Box 1:
The Autoscale scale out rule increases the number of VMs by 2 if the CPU threshold is 80% or higher. The initial instance count is 4 and rises to 6 when the 2 extra instances of VMs are added.
Box 2:
The Autoscale scale in rule decreases the number of VMs by 4 if the CPU threshold is 30% or lower. The initial instance count is 4 and thus cannot be reduced to 0 as the minimum instances is set to 2. Instances are only added when the CPU threshold reaches 80%.
References:
https://docs.microsoft.com/en-us/azure/azure-monitor/platform/autoscale-overview
https://docs.microsoft.com/en-us/azure/azure-monitor/platform/autoscale-best-practices
https://docs.microsoft.com/en-us/azure/azure-monitor/platform/autoscale-common-scale-patterns

NEW QUESTION: 4
RSE6500 support auxiliary flow progress bar index, users can index through the progress bar, quickly locate to video key information to playback. ( )
A. TRUE
B. FALSE
Answer: A