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NEW QUESTION: 1
For marketing purposes, your customer plans to create target groups based on attributes hobbies, region, and sales volume.
Which data sources do you suggest to your customer for segmentation?(Choose three)
A. Account fact sheet
B. Marketing attribute sets (from accounts)
C. Business Warehouse InfoCubes (SAP BW Query)
D. InfoSets
E. Interactive Reports
Answer: B,C,D

NEW QUESTION: 2
Ihr Netzwerk enthält eine Active Directory-Domäne mit dem Namen contoso.com. Die Domain enthält einen Mitgliedsserver
mit dem Namen Server1. Auf Server1 wird Windows Server 2012 R2 ausgeführt und die Hyper-V-Serverrolle installiert.
Server1 hostet 10 virtuelle Maschinen. Auf einer virtuellen Maschine mit dem Namen VM1 wird Windows Server 2012 R2 ausgeführt und ein Host ausgeführt
Prozessorintensive Anwendung mit dem Namen App1.
Benutzer berichten, dass App1 langsamer als erwartet reagiert.
Sie müssen die Prozessorauslastung auf VM1 überwachen, um festzustellen, ob Änderungen an den Hardwareeinstellungen vorgenommen werden müssen
von VM1.
Welches Leistungsobjekt sollten Sie auf Server1 überwachen?
A. Prozessor
B. Logischer Hyper-V-Hypervisor-Prozessor
C. Prozess
D. Virtueller Hyper-V-Hypervisor-Prozessor
E. Virtueller Hyper-V-Hypervisor-Stammprozessor
Answer: D
Explanation:
Erläuterung:
http://social.technet.microsoft.com/wiki/contents/articles/1234.hyper-v-concepts-vcpu-virtual-processor.aspx
http://blogs.msdn.com/b/tvoellm/archive/2008/05/12/hyper-v-performance-counters-part-four-of-many-hyper-v-
hypervisor-virtual-processor-and-hyper-v-hypervisor-root-virtual-processor-counter-set.aspx

NEW QUESTION: 3
Refer to the exhibit.

OSPF is configured on R1 and R2 as shown. Which action can you take to allow a neighbor relationship to be established.
A. On R1, set the network type of the FastEthernet0/0.12 interface to broadcast
B. Configure R1 and R2 as neighbors using router IDs.
C. On R1, change the process ID to 1
D. Configure R1 and R2 as neighbors using interface addresses.
Answer: A

NEW QUESTION: 4
A company plans to use Platform-as-a-Service (PaaS) to create the new data pipeline process. The process must meet the following requirements.
Ingest:
*Access multiple data sources
*Provide the ability to orchestrate workflow
*Provide the capability to run SQL Server Integration Services packages.
Store:
*Optimize storage for big data workloads.
*Provide encryption of data at rest.
*Operate with no size limits.
Prepare and Train:
*Provide a fully-managed and interactive workspace for exploration and visualization.
*Provide the ability to program in R, SQL, Python, Scala, and Java.
*Provide seamless user authentication with Azure Active Directory.
Model & Serve:
*Implement native columnar storage.
*Support for the SQL language
*Provide support for structured streaming.
You need to build the data integration pipeline.
Which technologies should you use? To answer, select the appropriate options in the answer area.

Answer:
Explanation:

Explanation

Ingest: Azure Data Factory
Azure Data Factory pipelines can execute SSIS packages.
In Azure, the following services and tools will meet the core requirements for pipeline orchestration, control flow, and data movement: Azure Data Factory, Oozie on HDInsight, and SQL Server Integration Services (SSIS).
Store: Data Lake Storage
Data Lake Storage Gen1 provides unlimited storage.
Note: Data at rest includes information that resides in persistent storage on physical media, in any digital format. Microsoft Azure offers a variety of data storage solutions to meet different needs, including file, disk, blob, and table storage. Microsoft also provides encryption to protect Azure SQL Database, Azure Cosmos DB, and Azure Data Lake.
Prepare and Train: Azure Databricks
Azure Databricks provides enterprise-grade Azure security, including Azure Active Directory integration.
With Azure Databricks, you can set up your Apache Spark environment in minutes, autoscale and collaborate on shared projects in an interactive workspace. Azure Databricks supports Python, Scala, R, Java and SQL, as well as data science frameworks and libraries including TensorFlow, PyTorch and scikit-learn.
Model and Serve: SQL Data Warehouse
SQL Data Warehouse stores data into relational tables with columnar storage.
Azure SQL Data Warehouse connector now offers efficient and scalable structured streaming write support for SQL Data Warehouse. Access SQL Data Warehouse from Azure Databricks using the SQL Data Warehouse connector.
References:
https://docs.microsoft.com/bs-latn-ba/azure/architecture/data-guide/technology-choices/pipeline-orchestration-da
https://docs.microsoft.com/en-us/azure/azure-databricks/what-is-azure-databricks