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NEW QUESTION: 1
You need to configure the data entry and business intelligence databases.
In the table below, identify the option that you must use for each database.
NOTE: Make only one selection in each column.

Answer:
Explanation:

Explanation

Data Entry: Geo-replicated database only
From Contoso scenario: Each location database for the data entry application may have an unpredictable amount of activity. Data must be replicated to secondary databases in Azure datacenters in different regions.
Business intelligence: Elastic database pools only
From Contoso scenario: For the business intelligence application, corporate executives must be able to view all data in near real-time with low network latency.
SQL DB elastic pools provide a simple cost effective solution to manage the performance goals for multiple databases that have widely varying and unpredictable usage patterns.
References:https://docs.microsoft.com/en-us/azure/sql-database/sql-database-elastic-pool
Topic 3, SQL Server ReportingBackground
You manage a Microsoft SQL Server environment that includes the following databases: DB1, DB2, Reporting.
The environment also includes SQL Reporting Services (SSRS) and SQL Server Analysis Services (SSAS).
All SSRS and SSAS servers use named instances. You configure a firewall rule for SSAS.
Databases
Database Name:
DB1
Notes:
This database was migrated from SQL Server 2012 to SQL Server 2016. Thousands of records are inserted into DB1 or updated each second. Inserts are made by many different external applications that your company's developers do not control. You observe that transaction log write latency is a bottleneck in performance. Because of the transient nature of all the data in this database, the business can tolerate some data loss in the event of a server shutdown.
Database Name:
DB2
Notes:
This database was migrated from SQL Server 2012 to SQL Server 2016. Thousands of records are updated or inserted per second. You observe that the WRITELOG wait type is the highest aggregated wait type. Most writes must have no tolerance for data loss in the event of a server shutdown. The business has identified certain write queries where data loss is tolerable in the event of a server shutdown.
Database Name:
Reporting
Notes:
You create a SQL Server-authenticated login named BIAppUser on the SQL Server instance to support users of the Reporting database. The BIAppUser login is not a member of the sysadmin role.
You plan to configure performance-monitoring alerts for this instance by using SQL Agent Alerts.

NEW QUESTION: 2
A NetBackup policy is configured to back up the following paths for a client:
D:\stage
E:\data
E:\home
F:\production
Multistreaming is enabled for the backups, and multiplexing is set to 4. Backup streams are running slowly.
Which step should the administrator consider to improve backup performance?
A. back up only one stream from each physical device on the client
B. disable multistreaming
C. increase the multiplexing level
D. increase the number of data buffers
Answer: A

NEW QUESTION: 3
Microsoft AzureでLambdaアーキテクチャを使用してソリューションを開発しています。
テストレイヤーのデータは、次の要件を満たしている必要があります。
データストレージ：
*リポジトリ（またはさまざまな形式の大量の大容量ファイル）として機能します。
*ビッグデータ分析ワークロード用に最適化されたストレージを実装します。
*階層構造を使用してデータを整理できるようにします。
バッチ処理：
*メモリ内の計算処理にはマネージドソリューションを使用します。
* Scala、Python、およびRプログラミング言語をネイティブにサポートします。
*クラスターを自動的にサイズ変更および終了する機能を提供します。
Analytical data store:
* Support parallel processing.
* Use columnar storage.
* Support SQL-based languages.
Lambdaアーキテクチャを構築するには、正しいテクノロジーを特定する必要があります。
どのテクノロジーを使用する必要がありますか？回答するには、回答エリアで適切なオプションを選択します
注：それぞれの正しい選択には1ポイントの価値があります。

Answer:
Explanation:

Explanation

Data storage: Azure Data Lake Store
A key mechanism that allows Azure Data Lake Storage Gen2 to provide file system performance at object storage scale and prices is the addition of a hierarchical namespace. This allows the collection of objects/files within an account to be organized into a hierarchy of directories and nested subdirectories in the same way that the file system on your computer is organized. With the hierarchical namespace enabled, a storage account becomes capable of providing the scalability and cost-effectiveness of object storage, with file system semantics that are familiar to analytics engines and frameworks.
Batch processing: HD Insight Spark
Aparch Spark is an open-source, parallel-processing framework that supports in-memory processing to boost the performance of big-data analysis applications.
HDInsight is a managed Hadoop service. Use it deploy and manage Hadoop clusters in Azure. For batch processing, you can use Spark, Hive, Hive LLAP, MapReduce.
Languages: R, Python, Java, Scala, SQL
Analytic data store: SQL Data Warehouse
SQL Data Warehouse is a cloud-based Enterprise Data Warehouse (EDW) that uses Massively Parallel Processing (MPP).
SQL Data Warehouse stores data into relational tables with columnar storage.
References:
https://docs.microsoft.com/en-us/azure/storage/blobs/data-lake-storage-namespace
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/batch-processing
https://docs.microsoft.com/en-us/azure/sql-data-warehouse/sql-data-warehouse-overview-what-is

NEW QUESTION: 4
Which of the following regarding the location of an MPLS label is FALSE?
A. The MPLS label value may be carried in a Frame Relay header.
B. The MPLS label value may be carried in a shim header.
C. The MPLS label value may be carried in an ATM header.
D. The MPLS label value may be carried in an OSPF header.
Answer: D