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NEW QUESTION: 1
Your network consists of a single Active Directory domain. The network contains a file server that runs Windows Server 2008 R2. All servers use internal storage only. You plan to deploy a client/server application.
You need to deploy the application so that it is available if a single server fails. You must achieve this goal while minimizing costs.
What should you do?
A. Deploy a failover cluster that uses Node and File Share Disk Majority.
B. Deploy Distributed File System (DFS) and configure replication.
C. Deploy a failover cluster that uses No Majority: Disk Only.
D. Deploy RemoteApp.
Answer: A
Explanation:
Explanation
Explanation/Reference:
HIGH AVAILABILITY OF APPLICATION/SERVICE
A client/server application is an application that uses a central place (server) to store its data but is accessed from one or more client computers, e.g. SQL, Outlook/Microsoft Exchange, dynamic web-based applications.
Failover clustering provides high availability for client/server applications, DFS Namespace Server, DHCP Server, Distributed Transaction Coordinator (DTC), File Server, Internet Storage Name Service (iSNS) Server, Message Queuing, Print Server, Remote Desktop Connection Broker (formerly TS Session Broker), Virtual Machine and WINS Server.
Failover clustering is configured according to quorum. Quorum for a cluster is determined by the number of voting elements (active cluster members) for that cluster to start properly or continue running. Cluster configuration is stored in the nodes, disk witness and/or file share witness. There are 4 quorum configurations:
1.Node Majority (recommended for clusters with an odd number of nodes)
2.Node and Disk Majority (recommended for clusters with an even number of nodes)
3.Node and File Share Majority: An alternative to Node and Disk Majority by using a file share witness instead of a disk witness.
4.No Majority: Disk Only (not recommended): Can sustain failures of all nodes except one (if the disk is online). However, this configuration is not recommended because the disk might be a single point of failure. (Comment: But how about providing high availability for the disk witness, e.g. RAID 1/5 with MPIO? Such a configuration maximizes the availability of the nodes!)



http://technet.microsoft.com/en-us/library/cc731960.aspx http://technet.microsoft.com/en-us/library/cc731739.aspx

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 the series.
Start of repeated scenario
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 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.
The following requirements must be met:
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.
End of repeated scenario
You need to implement the data partitioning strategy.
How should you partition the Fact.Order table?
A. Use a granularity of one month.
B. Create 17,520 partitions.
C. Create 2,557 partitions.
D. Create 1,460 partitions.
Answer: C
Explanation:
Explanation/Reference:
Explanation:
We create on partition for each day, which means that a granularity of one day is used.
Note: If we calculate the partitions that are needed, we get: 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: 3

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