MCQs on Data Processing with Flink | Apache Flink MCQs Questions
Apache Flink is a powerful stream-processing framework for processing data in real-time and batch modes. Chapter 3 delves into core topics such as stream vs. batch processing, handling late data, aggregations, joins, and advanced operations like custom operators. Test your knowledge with these carefully curated MCQs, ideal for beginners and experts alike.
Stream Processing vs Batch Processing in Flink
- What is the key characteristic of stream processing in Flink?
a) Processes data as it arrives
b) Processes data in fixed intervals
c) Only supports static datasets
d) Requires batch files as input - Which processing mode is more suitable for real-time analytics in Flink?
a) Batch processing
b) Stream processing
c) File processing
d) None of the above - In Flink, what defines batch processing?
a) Continuous data processing
b) Processing finite datasets
c) Event-driven architecture
d) Processing in real-time - Stream processing is primarily used for:
a) Historical analysis
b) Processing static data
c) Real-time data processing
d) Processing outdated datasets - Which Flink feature is ideal for combining stream and batch workloads?
a) Hybrid architecture
b) Flink SQL
c) Unified data processing
d) Task chaining
Handling Late Data and Out-of-Order Events
- How does Flink handle late data in stream processing?
a) By discarding it
b) By using watermarks
c) By reprocessing the entire dataset
d) By pausing the stream - What are watermarks in Flink?
a) Indicators of event time progress
b) Markers for state partitioning
c) Metrics for performance monitoring
d) Identifiers for batch boundaries - Which configuration helps in handling out-of-order events in Flink?
a) Keyed streams
b) Sliding windows
c) Event time characteristics
d) Parallelism adjustment - Late data can be processed using:
a) Timeout settings
b) Allowed lateness
c) Timestamp overrides
d) Stateful operators - Flink discards late events when:
a) Watermarks pass their event time
b) Parallelism is reduced
c) Checkpointing is enabled
d) Backpressure occurs
Aggregations, Joins, and Stateful Operations
- What is a keyed stream in Flink used for?
a) Stateless operations
b) Partitioning based on a key
c) Real-time data visualization
d) Scheduling tasks - Aggregations in Flink work on:
a) Non-keyed streams only
b) Both keyed and non-keyed streams
c) Source streams exclusively
d) Batch data only - Which operation is used for joining two streams in Flink?
a) Merge
b) CoGroup
c) Interval join
d) Reduce - What is the purpose of stateful operations in Flink?
a) Storing intermediate results
b) Partitioning data
c) Discarding late events
d) Generating watermarks - Which is a valid aggregation in Flink?
a) Count
b) Collect
c) Sample
d) Snapshot
Custom Operators and User-Defined Functions
- What is the main purpose of a user-defined function (UDF) in Flink?
a) Optimizing built-in operators
b) Implementing custom logic
c) Managing watermarks
d) Scheduling tasks - UDFs in Flink can be:
a) Stateless only
b) Stateful or stateless
c) Used for batch processing only
d) Limited to Java - Which interface is used for defining a FlatMap function in Flink?
a) FlatMapFunction
b) MapFunction
c) ReduceFunction
d) AggregateFunction - What is the role of custom operators in Flink?
a) Managing clusters
b) Enhancing state management
c) Enabling user-defined processing logic
d) Performing windowing operations - How are UDFs implemented in Flink?
a) As abstract classes
b) As anonymous functions
c) By implementing specific interfaces
d) By using key-value pairs
Partitioning and Parallelism
- Partitioning in Flink is primarily used for:
a) Watermark generation
b) Data redistribution
c) Reducing backpressure
d) Time-based processing - What determines parallelism in a Flink job?
a) The cluster size
b) Number of operators
c) Degree of data partitioning
d) Number of network buffers - KeyBy operation in Flink is used for:
a) Sorting streams
b) Grouping streams by a key
c) Generating watermarks
d) Partitioning data across jobs - What is the default partitioning strategy in Flink?
a) Shuffle
b) Keyed
c) Hash
d) Round-robin - Parallelism in Flink refers to:
a) Task distribution across nodes
b) Data aggregation across partitions
c) Real-time batch processing
d) Stream synchronization
Answers Table
| QNo | Answer |
|---|---|
| 1 | a) Processes data as it arrives |
| 2 | b) Stream processing |
| 3 | b) Processing finite datasets |
| 4 | c) Real-time data processing |
| 5 | c) Unified data processing |
| 6 | b) By using watermarks |
| 7 | a) Indicators of event time progress |
| 8 | c) Event time characteristics |
| 9 | b) Allowed lateness |
| 10 | a) Watermarks pass their event time |
| 11 | b) Partitioning based on a key |
| 12 | b) Both keyed and non-keyed streams |
| 13 | c) Interval join |
| 14 | a) Storing intermediate results |
| 15 | a) Count |
| 16 | b) Implementing custom logic |
| 17 | b) Stateful or stateless |
| 18 | a) FlatMapFunction |
| 19 | c) Enabling user-defined processing logic |
| 20 | c) By implementing specific interfaces |
| 21 | b) Data redistribution |
| 22 | c) Degree of data partitioning |
| 23 | b) Grouping streams by a key |
| 24 | d) Round-robin |
| 25 | a) Task distribution across nodes |
Stream Processing vs Batch Processing in Flink
- Which programming model does Flink use for batch processing?
a) MapReduce
b) Bulk synchronous parallel
c) Stream model
d) Parallel loop model
Handling Late Data and Out-of-Order Events
- In Flink, out-of-order events are primarily addressed using:
a) Event time and watermarks
b) Processing time windows
c) System clock synchronization
d) Batch reprocessing
Aggregations, Joins, and Stateful Operations
- What is a window join in Flink?
a) Joining two streams based on the same timestamp
b) Joining two streams within a specified time window
c) Joining static datasets
d) Joining streams without keys
Custom Operators and User-Defined Functions
- Which annotation is used to mark a UDF as rich in Flink?
a) @RichFunction
b) @RichUDF
c) No specific annotation
d) @CustomFunction
Partitioning and Parallelism
- Rescaling in Flink helps to:
a) Dynamically adjust parallelism during runtime
b) Partition streams by key
c) Reduce checkpoint intervals
d) Manage memory backpressure
Updated Answers Table
| QNo | Answer |
|---|---|
| 26 | b) Bulk synchronous parallel |
| 27 | a) Event time and watermarks |
| 28 | b) Joining two streams within a specified time window |
| 29 | c) No specific annotation |
| 30 | a) Dynamically adjust parallelism during runtime |