MCQs on Data Flows and Transformations (Basic) | Azure Data Factory MCQs Question

Azure Data Factory (ADF) is a cloud-based data integration service designed to streamline ETL (Extract, Transform, Load) processes and automate workflows. Chapter 5 focuses on the fundamentals of data flows and transformations in ADF, covering topics like mapping data flows, applying basic transformations such as filter, select, and sort, as well as debugging and monitoring data flows. These Azure Data Factory MCQs questions will help learners grasp the core concepts, improve data flow efficiency, and understand the distinctions between data flows and activities in ADF.

Introduction to Data Flows in ADF

1. What is the primary purpose of data flows in Azure Data Factory?
   a) Storing data
   b) Visualizing data transformations
   c) Managing user access
   d) Creating dashboards
2. What type of data transformation is typically performed in data flows?
   a) Real-time processing
   b) Batch processing
   c) Network routing
   d) Data backup
3. Which feature allows you to configure data flow transformations without writing code in ADF?
   a) Logic Apps
   b) Mapping Data Flows
   c) Power Query
   d) Spark SQL
4. What is the default compute used in ADF data flows?
   a) Apache Hive clusters
   b) Azure Synapse Analytics
   c) Azure Data Factory Integration Runtime
   d) Azure Kubernetes Service
5. Which type of ADF pipeline uses data flows as its building block?
   a) Data-driven pipelines
   b) Pipeline triggers
   c) ETL pipelines
   d) Event-based pipelines

Creating Mapping Data Flows

6. What is the first step when creating a mapping data flow in ADF?
   a) Define transformations
   b) Select a sink dataset
   c) Choose a source dataset
   d) Monitor pipeline
7. Which element in a mapping data flow represents the output destination?
   a) Source
   b) Sink
   c) Stream
   d) Aggregate
8. How can you specify the schema for a dataset in mapping data flows?
   a) Using Data Preview
   b) By importing from a linked service
   c) Through schema drift
   d) Manually entering column names
9. What action allows data from two datasets to be combined in mapping data flows?
   a) Join
   b) Aggregate
   c) Split
   d) Cache
10. What is a primary use case for lookup transformations in data flows?
    a) Filtering rows
    b) Selecting specific columns
    c) Enriching data from external sources
    d) Sorting data alphabetically

Basic Transformations: Filter, Select, Sort

11. Which transformation in ADF allows you to include only rows meeting specific conditions?
    a) Select
    b) Sort
    c) Filter
    d) Aggregate
12. What does the Select transformation enable you to do?
    a) Change column data types
    b) Drop unused columns
    c) Rename columns
    d) All of the above
13. How does the Sort transformation in data flows affect performance?
    a) By splitting data across nodes
    b) By requiring memory for sorting operations
    c) By automatically caching results
    d) By compressing data during sorting
14. What transformation is typically used to group and calculate data in ADF?
    a) Aggregate
    b) Union
    c) Join
    d) Cache
15. Which transformation would you use to reformat dates in a dataset?
    a) Derived Column
    b) Filter
    c) Sort
    d) Select

Data Flow Debugging and Monitoring

16. What does the debug feature in ADF data flows allow users to do?
    a) Execute pipelines in production mode
    b) Preview transformations with live data
    c) Pause pipeline execution
    d) Schedule data flow triggers
17. What is required to enable data flow debugging?
    a) A linked service to Azure Data Lake
    b) A Spark cluster
    c) An active Integration Runtime
    d) A Power BI license
18. Where can you view detailed metrics for executed data flows?
    a) Data Preview tab
    b) Monitor tab in ADF
    c) Pipeline Settings
    d) Linked Service Configuration
19. Which metric indicates the number of rows processed in a data flow?
    a) Memory Usage
    b) Data Shuffling
    c) Row Count
    d) Error Rate
20. What does enabling “Data Preview” in a mapping data flow help with?
    a) Running the data flow at scale
    b) Viewing transformation results on a sample dataset
    c) Deploying to production
    d) Configuring sink datasets

Differences Between Data Flows and Activities

21. What is the primary difference between activities and data flows in ADF?
    a) Activities perform actions; data flows transform data
    b) Data flows are faster than activities
    c) Activities require manual coding, while data flows don’t
    d) Data flows can only be used with structured data
22. Which of these is an example of an activity in ADF?
    a) Lookup
    b) Filter
    c) Sort
    d) Aggregate
23. How are data flows integrated into pipelines in ADF?
    a) As linked services
    b) Using the “Data Flow” activity
    c) Through Spark SQL queries
    d) As triggers
24. Which activity is commonly used to copy data in ADF?
    a) Data Flow
    b) Copy Data
    c) Lookup
    d) Append
25. What is a limitation of data flows compared to activities?
    a) Cannot handle unstructured data
    b) Require specific compute resources
    c) Cannot be monitored
    d) Limited to Azure Synapse

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