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NEW QUESTION: 1
Chatterフィードの場所を変更できますか？どうやって？
A. いいえ。ただし、ユーザーの好みに応じて非表示または表示できます。
B. 上記のいずれでもない
C. はい。ページレイアウトの使用
D. はい。ただし、管理者ユーザーのみ
Answer: A
NEW QUESTION: 2
If
A. Option D
B. Option A
C. Option B
D. Option E
E. Option C
Answer: B
Explanation:
Explanation/Reference:
To go from you would multiply the exponent ofg(2/5)by(-1/2)Therefore, to
go from 16 (the value ofg(2/5)to the value ofg(-1/5)multiply the exponent of 16 by(-1/5)The exponent of 16
is one, so the value ofg(-1/5) = 16to the(-1/2)power, which is1/4.
NEW QUESTION: 3
What is the output of the Initialize Array function after the following code has executed?
A. b.1-D Array of {4, 4, 4}
B. a.1-D Array of {3, 3, 3, 3}
C. d.1-D Array of {4, 3}
D. c.1-D Array of {3, 4}
Answer: B
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