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NEW QUESTION: 1
You are developing an application that will transmit large amounts of data between a client computer and a server.
You need to ensure the validity of the data by using a cryptographic hashing algorithm.
Which algorithm should you use?
A. Aes
B. HMACSHA256
C. DES
D. RNGCryptoServiceProvider
Answer: B
Explanation:
The .NET Framework provides the following classes that implement hashing algorithms:
* HMACSHA1.
* MACTripleDES.
* MD5CryptoServiceProvider.
* RIPEMD160.
* SHA1Managed.
* SHA256Managed.
* SHA384Managed.
* SHA512Managed.
HMAC variants of all of the Secure Hash Algorithm (SHA), Message Digest 5 (MD5), and RIPEMD-160 algorithms.
CryptoServiceProvider implementations (managed code wrappers) of all the SHA algorithms.
Cryptography Next Generation (CNG) implementations of all the MD5 and SHA algorithms.
Reference: http://msdn.microsoft.com/en-us/library/92f9ye3s.aspx#hash_values
NEW QUESTION: 2
An EMC Data Domain customer wants to use the Extended Retention software option with their new Data Domain system.
When sizing for the archive tier, what is the maximum number of future years that should be considered?
A. 0
B. 1
C. 2
D. 3
Answer: B
NEW QUESTION: 3
会社は、カスタムソリューションを使用して、オンプレミスのMicrosoft SQL Serverパイプラインを管理します。
データエンジニアリングチームは、SQL Serverからデータを取得してAzure Blobストレージに移行するプロセスを実装する必要があります。プロセスは、データのライフサイクルを調整および管理する必要があります。
オンプレミスのSQL Serverデータベースに接続するようにAzure Data Factoryを構成する必要があります。
順番に実行する必要がある3つのアクションはどれですか？回答するには、適切なアクションをアクションのリストから回答エリアに移動し、正しい順序に並べます。
Answer:
Explanation:
Explanation
Step 1: Create a virtual private network (VPN) connection from on-premises to Microsoft Azure.
You can also use IPSec VPN or Azure ExpressRoute to further secure the communication channel between your on-premises network and Azure.
Azure Virtual Network is a logical representation of your network in the cloud. You can connect an on-premises network to your virtual network by setting up IPSec VPN (site-to-site) or ExpressRoute (private peering).
Step 2: Create an Azure Data Factory resource.
Step 3: Configure a self-hosted integration runtime.
You create a self-hosted integration runtime and associate it with an on-premises machine with the SQL Server database. The self-hosted integration runtime is the component that copies data from the SQL Server database on your machine to Azure Blob storage.
Note: A self-hosted integration runtime can run copy activities between a cloud data store and a data store in a private network, and it can dispatch transform activities against compute resources in an on-premises network or an Azure virtual network. The installation of a self-hosted integration runtime needs on an on-premises machine or a virtual machine (VM) inside a private network.
References:
https://docs.microsoft.com/en-us/azure/data-factory/tutorial-hybrid-copy-powershell
NEW QUESTION: 4
HOTSPOT
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 FactOrder 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.
* 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 FactOrder 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 optimize data loading for the Dimension.SalesTerritory, Dimension.Customer, and Dimension.Date tables.
Which technology should you use for each table?
To answer, select the appropriate technologies in the answer area.
Answer:
Explanation:
Explanation:
Box 1: Temporal table
Box 2: Temporal table
Compared to CDC, Temporal tables are more efficient in storing historical data as it ignores insert actions.
Box 3: Change Data Capture (CDC)
By using change data capture, you can track changes that have occurred over time to your table. This kind of functionality is useful for applications, like a data warehouse load process that need to identify changes, so they can correctly apply updates to track historical changes over time.
CDC is good for maintaining slowly changing dimensions.
Scenario: Optimize data loading for the Dimension.SalesTerritory, Dimension.Customer, and Dimension.Date tables.
The Dimension.SalesTerritory and Dimension.Customer tables are frequently updated.
References:
https://www.mssqltips.com/sqlservertip/5212/sql-server-temporal-tables-vs-change-data- capture-vs-change-tracking--part-2/
https://docs.microsoft.com/en-us/sql/relational-databases/tables/temporal-table-usage- scenarios?view=sql-server-2017
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