Microsoft Certified Azure Data Engineer Associate · Free Practice Question Medium
Question 7
You can natively perform data transformations with Azure Synapse pipelines code free using the Mapping Data Flow task. Mapping Data Flows provide a fully visual experience with no coding required. Your data flows will run on your own execution cluster for scaled-out data processing.
Data flow activities can be operationalized via which of the following? (Select four)
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A
Manage hub
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B
Monitoring capabilities
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C
Control capabilities
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D
Data hub
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E
Monitor hub
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F
Flow capabilities
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G
Integrate hub
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H
Data Factory scheduling
Reveal correct answers
Correct answers: B, C, F, H
Explanation
You can natively perform data transformations with Azure Synapse pipelines code free using the Mapping Data Flow task. Mapping Data Flows provide a fully visual experience with no coding required. Your data flows will run on your own execution cluster for scaled-out data processing. Data flow activities can be operationalized via existing Data Factory scheduling, control, flow, and monitoring capabilities.
When building data flows, you can enable debug mode, which turns on a small interactive Spark cluster. Turn on debug mode by toggling the slider at the top of the authoring module. Debug clusters take a few minutes to warm up, but can be used to interactively preview the output of your transformation logic.
With the Mapping Data Flow added, and the Spark cluster running, this will enable you to perform the transformation, and run and preview the data. No coding is required as Azure Data Factory handles all the code translation, path optimization, and execution of your data flow jobs.
Note:
If your dataset is pointing at a folder with other files and you only want to use one file, you may need to create another dataset or utilize parameterization to make sure only a specific file is read
If you have not imported your schema in your ADLS, but have already ingested your data, go to the dataset's 'Schema' tab and click 'Import schema' so that your data flow knows the schema projection.
Mapping Data Flow follows an extract, load, transform (ELT) approach and works with staging datasets that are all in Azure. Currently the following datasets can be used in a source transformation:
Azure Blob Storage (JSON, Avro, Text, Parquet)
Azure Data Lake Storage Gen1 (JSON, Avro, Text, Parquet)
Azure Data Lake Storage Gen2 (JSON, Avro, Text, Parquet)
Azure Synapse Analytics
Azure SQL Database
Azure CosmosDB
Azure Data Factory has access to over 80 native connectors. To include data from those other sources in your data flow, use the Copy Activity to load that data into one of the supported staging areas.
Once your debug cluster is warmed up, verify your data is loaded correctly via the Data Preview tab. Once you click the refresh button, Mapping Data Flow will show a snapshot of what your data looks like when it is at each transformation.
A. The Manage hub is not directly related to operationalizing data flow activities. It focuses on managing and administering the overall Azure Synapse environment, rather than specifically handling the execution and monitoring of data flow activities.
B. Monitoring capabilities allow you to track the performance and status of data flow activities in real-time. This includes monitoring data flow execution, identifying bottlenecks or issues, and ensuring that the data processing is running smoothly.
C. Control capabilities enable you to manage and control the execution of data flow activities, including defining dependencies between different data flows, setting up triggers for automated execution, and handling error handling and retries.
D. The Data hub is not specifically designed for operationalizing data flow activities. It is more focused on storing and managing data assets within Azure Synapse, rather than controlling the execution and monitoring of data flow activities.
E. The Monitor hub is not directly related to operationalizing data flow activities. It focuses on monitoring the performance and health of the Azure Synapse environment as a whole, rather than specifically tracking the execution of data flow activities.
F. Flow capabilities refer to the ability to design and configure the data flow logic within Azure Synapse pipelines. This includes defining the transformations, aggregations, and data manipulations that need to be performed on the data as it flows through the pipeline.
G. The Integrate hub is not directly related to operationalizing data flow activities in Azure Synapse pipelines. It focuses on integrating data from various sources and systems, rather than managing the execution and monitoring of data flow activities.
H. Data Factory scheduling allows you to schedule and orchestrate the execution of data flow activities within Azure Synapse pipelines. It provides the capability to define when and how often the data flows should run, ensuring efficient data processing.
Discussion
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