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.
NOTE: Each correct selection is worth one point.

Correct Answer:

Explanation
Graphical user interface, application, table, email Description automatically generated

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You have an Azure subscription that contains an Azure Synapse Analytics dedicated SQL pool. You plan to deploy a solution that will analyze sales data and include the following:
* A table named Country that will contain 195 rows
* A table named Sales that will contain 100 million rows
* A query to identify total sales by country and customer from the past 30 days You need to create the tables. The solution must maximize query performance.
How should you complete the script? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Correct Answer:

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You plan to monitor an Azure data factory by using the Monitor & Manage app.
You need to identify the status and duration of activities that reference a table in a source database.
Which three actions should you perform in sequence? To answer, move the actions from the list of actions to the answer are and arrange them in the correct order.

Correct Answer:

Explanation

Step 1: From the Data Factory authoring UI, generate a user property for Source on all activities.
Step 2: From the Data Factory monitoring app, add the Source user property to Activity Runs table.
You can promote any pipeline activity property as a user property so that it becomes an entity that you can monitor. For example, you can promote the Source and Destination properties of the copy activity in your pipeline as user properties. You can also select Auto Generate to generate the Source and Destination user properties for a copy activity.
Step 3: From the Data Factory authoring UI, publish the pipelines
Publish output data to data stores such as Azure SQL Data Warehouse for business intelligence (BI) applications to consume.
References:
https://docs.microsoft.com/en-us/azure/data-factory/monitor-visually

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You are implementing a batch dataset in the Parquet format.
Data tiles will be produced by using Azure Data Factory and stored in Azure Data Lake Storage Gen2. The files will be consumed by an Azure Synapse Analytics serverless SQL pool.
You need to minimize storage costs for the solution.
What should you do?

• A.Store all the data as strings in the Parquet tiles.
• B.Use OPENROWEST to query the Parquet files.
• C.Create an external table mat contains a subset of columns from the Parquet files.
• D.Use Snappy compression for the files.

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Which Azure Data Factory components should you recommend using together to import the daily inventory data from the SQL server to Azure Data Lake Storage? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Correct Answer:

Explanation

Box 1: Self-hosted integration runtime
A self-hosted IR is capable of running copy activity between a cloud data stores and a data store in private network.
Box 2: Schedule trigger
Schedule every 8 hours
Box 3: Copy activity
Scenario:
Customer data, including name, contact information, and loyalty number, comes from Salesforce and can be imported into Azure once every eight hours. Row modified dates are not trusted in the source table.
Product data, including product ID, name, and category, comes from Salesforce and can be imported into Azure once every eight hours. Row modified dates are not trusted in the source table.

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