S03 DATA FACTORY: INGESTA, DATAFLOW GEN2 Y ORQUESTACION

What is a data pipeline?

• A special folder in OneLake storage where data can be exported from a lakehouse

• A sequence of activities to orchestrate a data ingestion or transformation process

• A saved Power Query

• none of the above

You want to use a pipeline to copy data to a folder with a specified name for each run. What should you do?

• Create multiple pipelines - one for each folder name

• Use a Dataflow (Gen2)

• Add a parameter to the pipeline and use it to specify the folder name for each run

• None of the above

You have previously run a pipeline containing multiple activities. What's the best way to check how long each individual activity took to complete?

• Rerun the pipeline and observe the output, timing each activity.

• View the run details in the run history.

• View the Refreshed value for your lakehouse's default dataset

• None of the above

In what scenario is Dataflows Gen2 preferred over traditional data pipelines for data ingestion and transformation?

• When row-level security is a critical requirement for the data processing.

• When you need to integrate data from multiple sources with complex transformations using a low-code interface.

• When the goal is to replace an existing data warehouse entirely.

• None of the above.

To implement an ELT process using Dataflows Gen2 and pipelines, what is the correct sequence of actions?

• Extract and load data with a pipeline, then transform data with a Dataflow Gen2

• Load data directly into Power BI, then transform using Dataflows Gen2

• Transform data with a Dataflow Gen2, then load it using a pipeline

• None of the above