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NEW QUESTION: 1
You are developing a solution using a Lambda architecture on Microsoft Azure.
The data at test layer must meet the following requirements:
Data storage:
*Serve as a repository (or high volumes of large files in various formats.
*Implement optimized storage for big data analytics workloads.
*Ensure that data can be organized using a hierarchical structure.
Batch processing:
*Use a managed solution for in-memory computation processing.
*Natively support Scala, Python, and R programming languages.
*Provide the ability to resize and terminate the cluster automatically.
Analytical data store:
*Support parallel processing.
*Use columnar storage.
*Support SQL-based languages.
You need to identify the correct technologies to build the Lambda architecture.
Which technologies should you use? To answer, select the appropriate options in the answer area NOTE: Each correct selection is worth one point.

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: 2
You need to resolve the sales representatives' report data issue.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

NEW QUESTION: 3
Sie verwalten einen Microsoft SQL Server 2014-Server. Eine der Datenbanken auf dem Server unterstützt eine hochaktive OLTP-Anwendung.
Benutzer melden ungewöhnlich lange Wartezeiten, wenn sie Daten an die Anwendung senden.
Sie müssen ermitteln, welche Abfragen länger als 1 Sekunde dauern, um über einen längeren Zeitraum ausgeführt zu werden.
Was tun?
A. Führen Sie den Befehl DBCC TRACEON 1222 in einem Abfragefenster aus und überprüfen Sie das SQL Server-Ereignisprotokoll.
B. Führen Sie den Befehl sp_who in einem Abfragefenster aus.
C. Verwenden Sie sp_configure, um einen Wert für den Schwellenwert für blockierte Prozesse festzulegen. Erstellen Sie eine erweiterte Ereignissitzung.
D. Verwenden Sie den Job Activity Monitor, um alle Prozesse zu überprüfen, die aktiv ausgeführt werden. Überprüfen Sie den Auftragsverlauf, um die Dauer der einzelnen Schritte zu ermitteln.
E. Verwenden Sie SQL Profiler, um alle Abfragen zu verfolgen, die auf dem Server verarbeitet werden. Filtern Sie Abfragen mit einem Duration-Wert von mehr als 1.000.
Answer: E