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NEW QUESTION: 1
What is used by the HP VAN SDN Controllers for internal communication between controllers?
A. Auth ToKen
B. Service Token
C. Admin ToKen
D. REST API Token
Answer: D
Explanation:
Reference:http://h20566.www2.hp.com/portal/site/hpsc/template.BINARYPORTLET/public/ kb/docDisplay/resource.process/?spf_p.tpst=kbDocDisplay_ws_BI&spf_p.rid_kbDocDispla y=docDisplayResURL&javax.portlet.begCacheTok=com.vignette.cachetoken&spf_p.rst_kb DocDisplay=wsrp-resourceState%3DdocId%253Demr_na-c040031142%257CdocLocale%253D&javax.portlet.endCacheTok=com.vignette.cachetoken(page 27)

NEW QUESTION: 2
Auto ScalingグループのEC2インスタンスで構成されるアプリケーションがあります。毎日特定の時間枠の間に、あなたのウェブサイトへのトラフィックが増加しています。したがって、ユーザーはアプリケーションの応答時間が遅いことに不満を持っています。 Auto Scalingグループを設定して、CPU使用率が5分間の2つの連続した期間で60％を超えたときに1つの新しいEC2インスタンスをデプロイしました。この問題を解決するための最も費用効果の低い方法は何ですか？
A. 収集期間の連続数を減らす
B. Auto Scalingグループのインスタンスの最小数を増やします
C. 新しいインスタンスをデプロイするためのしきい値CPU使用率を減らします
D. 収集期間を10分に減らします
Answer: B
Explanation:
Explanation
If you increase the minimum number of instances, then they will be running even though the load is not high on the website. Hence you are incurring cost even though there is no need.
All of the remaining options are possible options which can be used to increase the number of instances on a high load.
For more information on On-demand scaling, please refer to the below link:
* http://docs.aws.amazon.com/autoscaling/latest/userguide/as-scale-based-on-demand.
html
Note: The tricky part where the question is asking for 'least cost effective way". You got the design consideration correctly but need to be careful on how the question is phrased.

NEW QUESTION: 3
A standard cost variance has affected the general ledger accounts assigned to the lot size variance
type of a specific batch order.
What is the cause of a lot size variance?
A. different costs reported
B. changes to production bill of materials (BOM) or route
C. different batch quantity or constant costs
D. different quantity consumed
Answer: C

NEW QUESTION: 4
DRAG DROP
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 configure the Fact.Order table.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

Answer:
Explanation:

Explanation:

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.
Step 1: Create a partition function.
Using CREATE PARTITION FUNCTION is the first step in creating a partitioned table or index.
Step 2: Create a partition scheme based on the partition function.
To migrate SQL Server partition definitions to SQL Data Warehouse simply:
Step 3: Execute an ALTER TABLE command to specify the partition function.
References: https://docs.microsoft.com/en-us/azure/sql-data-warehouse/sql-data-warehouse-tables-partition