
Conditional Split Transformation in Mapping Data Flow in Azure data factory
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10 questions
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- Q1What is the primary function of a Conditional Split transformation in Azure Data Factory?Converting data from one format to anotherSpeeding up data processing by splitting large datasetsEncrypting sensitive data before processingRouting data rows to different streams based on matching conditions30s
- Q2How does a Conditional Split transformation determine which stream to route a data row to?By randomly assigning rows to different streamsBy using machine learning algorithms to classify data rowsBy splitting the data based on a predetermined orderBy evaluating user-defined expressions against each data row30s
- Q3The scenario presented in the video involves splitting data based on employee department information. In which format was the employee department information stored?JSON fileCSV fileProprietary data formatRelational database table30s
- Q4What is the purpose of creating a new data flow in Azure Data Factory for this scenario?To implement the Conditional Split transformation for routing employee dataTo design the user interface for data visualizationTo connect to the external storage location of the CSV fileTo create a backup of the employee data30s
- Q5Which of the following statements is TRUE about configuring a Conditional Split transformation?Pre-written functions are available for common splitting scenariosThe conditions are limited to mathematical operations on numerical dataOnly one condition can be specified for a Conditional Split transformationYou can define multiple conditions to route data to different streams30s
- Q6What is the primary function of a container in this context?To connect to external data sources outside of AzureTo group related data processing activities for better organizationTo provide a secure environment for running data pipelinesTo store and manage large datasets efficiently30s
- Q7What is the final output of the data flow designed in the video?An updated version of the original CSV file with department information modifiedSeparate files containing employee data filtered based on departmentA graphical representation of the employee department distributionA single file with all employee data and an additional department column30s
- Q8The video mentions using an expression to define the condition for splitting data. Can you provide an example of a simple expression used for this purpose?@contains(employeeDepartment, 'IT') (This expression checks if the 'employeeDepartment' field contains the text 'IT')employeeDepartment > 10 (This expression is not valid for string data)All of the above (There can be multiple valid expressions depending on the scenario)department = 'IT' (This assumes 'department' is the field name, which may not be the case)30s
- Q9What are some of the benefits of using Conditional Split transformations in Azure Data Factory?They are easy to implement and require minimal coding knowledgeImproved data processing efficiency by filtering data early in the pipelineThey can be used to combine data from multiple sources into a single streamConditional Split transformations are always the most efficient way to process data30s
- Q10The video mentions using the Azure Data Factory UI for configuring the data flow. Is it also possible to define data flows using code?The UI is the only way to create data flows in Azure Data FactoryUsing code requires additional software besides Azure Data FactoryCode is required for complex data flows, while the UI is suitable for simpler onesYes, Azure Data Factory supports defining data flows using code through Azure Data Factory Language (ADF)30s