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NEW QUESTION: 1
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-d
https://docs.microsoft.com/en-us/azure/azure-databricks/what-is-azure-databricks

NEW QUESTION: 2
Which of the following is a Problem Management activity?
A. Reactive support
B. Error Control
C. First contact resolution
D. SLA analysis
Answer: B

NEW QUESTION: 3
You are designing a data processing solution that will run as a Spark job on an HDInsight cluster. The solution will be used to provide near real-time information about online ordering for a retailer.
The solution must include a page on the company intranet that displays summary information.
The summary information page must meet the following requirements:
* Display a summary of sales to date grouped by product categories, price range, and review scope.
* Display sales summary information including total sales, sales as compared to one day ago and sales as compared to one year ago.
* Reflect information for new orders as quickly as possible.
You need to recommend a design for the solution.
What should you recommend? To answer, select the appropriate configuration in the answer area.

Answer:
Explanation:

Explanation

Box 1: DataFrame
DataFrames
Best choice in most situations.
Provides query optimization through Catalyst.
Whole-stage code generation.
Direct memory access.
Low garbage collection (GC) overhead.
Not as developer-friendly as DataSets, as there are no compile-time checks or domain object programming.
Box 2: parquet
The best format for performance is parquet with snappy compression, which is the default in Spark 2.x.
Parquet stores data in columnar format, and is highly optimized in Spark.