MCQs on Performance Optimization and Monitoring | Apache Flink MCQs Questions

Apache Flink is a powerful framework for stream and batch data processing. Mastering performance optimization and monitoring techniques is crucial for building efficient, scalable applications. This article features 30 multiple-choice questions (MCQs) from Chapter 6: Performance Optimization and Monitoring, covering topics like tuning, resource allocation, metrics, debugging, and execution profiling.

Tuning Flink Applications for Scalability and Performance

1. What is the primary benefit of tuning parallelism in a Flink application?
   a) Improved performance
   b) Reduced code size
   c) Simplified debugging
   d) Enhanced data integrity
2. Which configuration setting adjusts the number of processing slots in Flink?
   a) taskmanager.numberOfSlots
   b) jobmanager.slotAllocation
   c) flink.parallelism.level
   d) slot.allocation.count
3. What is the default parallelism level in Flink?
   a) 1
   b) 2
   c) 4
   d) 8
4. Which of the following best describes Flink’s task chaining mechanism?
   a) Combines multiple tasks into a single operator chain
   b) Enables distributed processing across nodes
   c) Allocates memory dynamically at runtime
   d) Optimizes shuffle operations for partitioning
5. Which technique minimizes latency in stream processing with Flink?
   a) Batch processing
   b) Task chaining
   c) Increasing checkpoint intervals
   d) Disabling fault tolerance

Memory Management and Resource Allocation

6. What is the primary goal of memory management in Flink?
   a) To reduce disk usage
   b) To optimize execution speed
   c) To allocate resources dynamically
   d) To ensure predictable application behavior
7. How can Flink’s memory be divided?
   a) JVM Heap and Managed Memory
   b) Managed Memory and External Memory
   c) Core Memory and Buffer Memory
   d) Heap Memory and Shared Memory
8. What type of memory is used for managing Flink state backends?
   a) Off-heap memory
   b) Heap memory
   c) External memory
   d) Buffered memory
9. Which configuration controls network buffer allocation in Flink?
   a) taskmanager.network.memory.fraction
   b) flink.networking.buffer.size
   c) jobmanager.network.allocation
   d) task.network.config
10. What happens when Flink applications exceed memory limits?
    a) Applications crash immediately
    b) Flink attempts garbage collection
    c) Processing is paused
    d) Flink dynamically allocates more memory

Metrics, Logging, and Monitoring with Flink Dashboard

11. Which tool is most commonly used for monitoring Flink jobs?
    a) Flink Dashboard
    b) Hadoop YARN
    c) Spark UI
    d) Grafana
12. What kind of metrics does Flink collect by default?
    a) CPU usage and Memory allocation
    b) Task execution time and Throughput
    c) Task success rate and Shuffle statistics
    d) All of the above
13. How are logs typically stored in a Flink cluster?
    a) Local storage on TaskManager nodes
    b) Remote cloud storage
    c) Kafka topics
    d) HDFS directories
14. What does the backpressure metric indicate in Flink?
    a) High CPU usage
    b) Data processing delays
    c) Memory contention
    d) Task failure
15. Which file stores the main logging configuration for Flink?
    a) flink-config.yaml
    b) log4j.properties
    c) logging.xml
    d) flink-logging.conf

Debugging and Troubleshooting Flink Applications

16. What is the first step in troubleshooting failed Flink jobs?
    a) Check the application logs
    b) Increase parallelism
    c) Restart the cluster
    d) Optimize resource allocation
17. Which log level provides the most detailed information?
    a) INFO
    b) DEBUG
    c) WARN
    d) ERROR
18. How can you capture stack traces for failed tasks in Flink?
    a) Enable stack trace logging in the configuration
    b) Use the stacktrace CLI tool
    c) Capture snapshots from the dashboard
    d) Inspect the job graph
19. What feature in Flink aids in identifying bottlenecks during execution?
    a) Execution graph
    b) Checkpointing
    c) Chaining optimizations
    d) Memory profiler
20. What is the primary cause of task failure due to backpressure?
    a) Network congestion
    b) Insufficient memory
    c) Slow downstream consumers
    d) Incorrect task parallelism

Profiling and Analyzing Execution Plans

21. What is Flink’s primary tool for analyzing execution plans?
    a) Web UI execution graph
    b) SQL Planner
    c) Execution history profiler
    d) Task chaining profiler
22. How can you view an execution plan before submitting a job?
    a) Use the explain() method
    b) Run flink-profile command
    c) Check Flink Dashboard
    d) Enable debug mode
23. Which visualization shows the task dependencies in a Flink job?
    a) Task execution graph
    b) Operator chain visualization
    c) Job flowchart
    d) DAG Viewer
24. What is the benefit of inspecting execution graphs in Flink?
    a) Detect performance bottlenecks
    b) Modify task parallelism dynamically
    c) Avoid resource contention issues
    d) Optimize stateful processing
25. What is the primary use of Flink’s web UI execution graph?
    a) Track job history
    b) Analyze failed tasks
    c) Debug data streams
    d) Monitor execution progress

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