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NEW QUESTION: 1
Your customer has 4,000 Widows-based network devices.
You need to recommend a tool to collect software deployment inventory.
Which tool should you recommend?
A. Microsoft License Advisor (MLA)
B. System Center Configuration Manager (ConfigMgr)
C. System Center Operations Manager (OpsMgr)
D. Microsoft Software Inventory Analyzer (MSIA)
Answer: B
Explanation:
Use software inventory in System Center 2012 Configuration Manager to collect information about files that are contained on client devices in your organization. Additionally, software inventory can collect files from client devices and store these on the site server. Software inventory is collected when the Enable software inventory on clients setting is enabled in client settings.
After software inventory is enabled and the clients run a software inventory cycle, the client sends the inventory information to a management point in the client's site. The management point then forwards the inventory information to the Configuration Manager site server, which stores the inventory information in the site database. Software inventory runs on clients according to the schedule that you specify in client settings.

NEW QUESTION: 2
DevOpsエンジニアは、AWS CodePipelineとAWS CodeBuildを使用してサーバーレスアプリケーションの継続的デプロイメントパイプラインを構築しています。ソース、ビルド、テストの各ステージは、デプロイステージを残して作成されています。同社は、特定の顧客のサブセットに展開し、すべての顧客に完全にリリースする前に監視することにより、失敗した展開のリスクを軽減したいと考えています。
これらの要件を満たすために、デプロイステージをどのように構成する必要がありますか？
A. AWS CloudFormationを使用して、スタックの更新ごとに新しいバージョンを公開します。 AWS：：Lambda：：AliasリソースのRoutingConfigプロパティを使用して、スタックの更新中にトラフィックルーティングを更新します。
B. CodeBuildを使用してAWS CLIを使用し、AWS Lambda関数コードを更新してから、関数の新しいバージョンを発行し、関数の新しいバージョンを指すようにプロダクションエイリアスを更新します。
C. AWS CloudFormationを使用してサーバーレスアプリケーションを定義し、AWS CodeDeployがDeploymentPreference：Canary10Percentl5Minutesを使用してAWS Lambda関数をデプロイします。
D. AWS CloudFormationを使用して、スタックの更新ごとに新しいバージョンを公開します。次に、開発者が新しいバージョンをテストして承認するためのCodePipeline承認アクションを設定します。最後に、CodePipeline呼び出しアクションを使用して、本番エイリアスを使用するようにAWS Lambda関数を更新します
Answer: C
Explanation:
https://docs.aws.amazon.com/codedeploy/latest/userguide/deployment-configurations.html

NEW QUESTION: 3

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

NEW QUESTION: 4
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 implement the data partitioning strategy.
How should you partition the Fact.Order table?
A. Create 17,520 partitions.
B. Create 2,557 partitions.
C. Create 730 partitions.
D. Use a granularity of two days.
Answer: B
Explanation:
We create on partition for each day. 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.
Maximize the performance during the data loading process for the Fact.Order partition.