Apache Flink is a powerful stream processing framework widely used for handling real-time data. Mastering its fundamentals is essential for developers and data engineers. This set of Apache Flink MCQ questions and answers covers key concepts such as the anatomy of a Flink application, DataStream API, windowing, and fault tolerance, helping you ace Flink interviews or exams.
Multiple Choice Questions
1. Anatomy of a Flink Application
What are the essential components of a Flink application? a) Source, Transformation, Sink b) Input, Output, Processor c) Data, Compute, Result d) Fetch, Process, Export
In a Flink program, the role of a sink is to: a) Read data b) Write data to an external system c) Transform data d) Filter data
Which component in a Flink application defines where data originates? a) Transformation b) Source c) Sink d) Operator
2. Flink’s DataStream API and DataSet API
The DataStream API is primarily used for: a) Batch processing b) Stream processing c) File system operations d) Machine learning tasks
Which API is better suited for bounded datasets? a) DataStream API b) DataSet API c) Table API d) SQL API
The DataSet API processes data in: a) Real-time b) Batches c) Small streams d) None of the above
3. Working with Streams: Source, Transformation, Sink
A transformation in Flink is used to: a) Change data format b) Define data flow between tasks c) Both a and b d) None of the above
Flink sources can read data from: a) Kafka topics b) Files c) Databases d) All of the above
What is the role of a sink in Flink? a) To visualize data b) To perform transformations c) To output processed data d) To manage checkpoints
4. Event Time vs Processing Time
What is event time in Flink? a) Time when the data is processed b) Timestamp associated with the data when it was created c) Time when data is written to a sink d) None of the above
Which time concept is more reliable for late data handling? a) Processing time b) System time c) Event time d) Wall clock time
What is processing time in Flink? a) Timestamp of when data was generated b) Time taken to process data c) Timestamp of when data was processed by the system d) None of the above
5. Windowing and Watermarks
Windows in Flink are used for: a) Defining data aggregation intervals b) Data visualization c) Fault tolerance d) None of the above
Which type of window triggers computations at fixed intervals? a) Sliding window b) Tumbling window c) Session window d) Global window
Watermarks in Flink are used to: a) Prevent data loss b) Mark boundaries of event time processing c) Reduce computation overhead d) Synchronize processing time
6. Fault Tolerance and Checkpointing
Fault tolerance in Flink is achieved through: a) Data replication b) Checkpointing c) Batch processing d) None of the above
Checkpoints are stored in: a) Memory only b) Persistent storage c) Local cache d) System logs
Flink’s checkpointing ensures: a) Low latency processing b) Exactly-once state consistency c) High throughput d) All of the above
Answer Key
QNo
Answer (Option with Text)
1
a) Source, Transformation, Sink
2
b) Write data to an external system
3
b) Source
4
b) Stream processing
5
b) DataSet API
6
b) Batches
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c) Both a and b
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d) All of the above
9
c) To output processed data
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b) Timestamp associated with the data when it was created
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c) Event time
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c) Timestamp of when data was processed by the system
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a) Defining data aggregation intervals
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b) Tumbling window
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b) Mark boundaries of event time processing
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b) Checkpointing
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b) Persistent storage
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b) Exactly-once state consistency
Additional Multiple Choice Questions
1. Anatomy of a Flink Application
Which of the following is not a valid component in a Flink application? a) ExecutionEnvironment b) PipelineFactory c) DataStream d) SinkFunction
Flink’s execution starts with: a) Defining a transformation b) Adding a sink c) Initializing the execution environment d) Registering a checkpoint
2. Flink’s DataStream API and DataSet API
The difference between DataStream API and DataSet API is: a) DataStream is for unbounded data, DataSet for bounded data b) DataStream is faster c) DataStream API works only with real-time data d) DataSet API has no transformations
Flink’s APIs support which programming languages? a) Java and Scala only b) Python, Java, and Scala c) C++ and Python d) JavaScript and Python
3. Working with Streams: Source, Transformation, Sink
A filter transformation in Flink is used to: a) Select specific fields from data b) Remove data that does not satisfy a condition c) Change the data type of a field d) Merge multiple streams
Flink transformations like keyBy and reduce work on: a) Raw streams b) Keyed streams c) Aggregated streams d) Filtered streams
4. Event Time vs Processing Time
What happens when data arrives late in event-time processing in Flink? a) It is dropped by default b) It is always processed c) It is handled based on watermark and allowed lateness d) Late data is not supported
Which Flink feature ensures proper handling of time-based operations? a) Event-time clocks b) Processing-time counters c) Watermarks d) Stateful processing
5. Windowing and Watermarks
A sliding window in Flink: a) Contains events that belong to non-overlapping time intervals b) Allows overlapping of events between windows c) Processes a single event multiple times d) Does not depend on event time
The difference between event-time windows and processing-time windows is: a) Event-time windows are less accurate b) Event-time windows rely on watermarks c) Processing-time windows handle late data better d) Event-time windows are only for batch processing
6. Fault Tolerance and Checkpointing
A checkpoint interval in Flink is configured to: a) Define the time taken for job execution b) Determine the frequency of state backup c) Limit the maximum number of transformations d) Adjust system throughput
In Flink, operator state is: a) Stored locally in the application b) Shared among all tasks in the application c) Managed by each task independently d) Stored only in memory
Updated Answer Key
QNo
Answer (Option with Text)
19
b) PipelineFactory
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c) Initializing the execution environment
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a) DataStream is for unbounded data, DataSet for bounded data
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b) Python, Java, and Scala
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b) Remove data that does not satisfy a condition
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b) Keyed streams
25
c) It is handled based on watermark and allowed lateness
