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Conditional Split Transformation in Mapping Data Flow in Azure data factory

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10 questions
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  • Q1
    What is the primary function of a Conditional Split transformation in Azure Data Factory?
    Converting data from one format to another
    Speeding up data processing by splitting large datasets
    Encrypting sensitive data before processing
    Routing data rows to different streams based on matching conditions
    30s
  • Q2
    How does a Conditional Split transformation determine which stream to route a data row to?
    By randomly assigning rows to different streams
    By using machine learning algorithms to classify data rows
    By splitting the data based on a predetermined order
    By evaluating user-defined expressions against each data row
    30s
  • Q3
    The scenario presented in the video involves splitting data based on employee department information. In which format was the employee department information stored?
    JSON file
    CSV file
    Proprietary data format
    Relational database table
    30s
  • Q4
    What 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 data
    To design the user interface for data visualization
    To connect to the external storage location of the CSV file
    To create a backup of the employee data
    30s
  • Q5
    Which of the following statements is TRUE about configuring a Conditional Split transformation?
    Pre-written functions are available for common splitting scenarios
    The conditions are limited to mathematical operations on numerical data
    Only one condition can be specified for a Conditional Split transformation
    You can define multiple conditions to route data to different streams
    30s
  • Q6
    What is the primary function of a container in this context?
    To connect to external data sources outside of Azure
    To group related data processing activities for better organization
    To provide a secure environment for running data pipelines
    To store and manage large datasets efficiently
    30s
  • Q7
    What is the final output of the data flow designed in the video?
    An updated version of the original CSV file with department information modified
    Separate files containing employee data filtered based on department
    A graphical representation of the employee department distribution
    A single file with all employee data and an additional department column
    30s
  • Q8
    The 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
  • Q9
    What are some of the benefits of using Conditional Split transformations in Azure Data Factory?
    They are easy to implement and require minimal coding knowledge
    Improved data processing efficiency by filtering data early in the pipeline
    They can be used to combine data from multiple sources into a single stream
    Conditional Split transformations are always the most efficient way to process data
    30s
  • Q10
    The 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 Factory
    Using code requires additional software besides Azure Data Factory
    Code is required for complex data flows, while the UI is suitable for simpler ones
    Yes, Azure Data Factory supports defining data flows using code through Azure Data Factory Language (ADF)
    30s

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