MCQs on Advanced Data Engineering with Spark | Apache Spark MCQs Questions

Apache Spark is a powerful framework for large-scale data processing. In this chapter, we delve into advanced topics, including partitioning, shuffling, and techniques to optimize joins and aggregations. Learn about broadcast variables, custom serialization, and strategies to handle data skews. The chapter also covers debugging and profiling for efficient Spark job management. These Apache Spark MCQs questions are tailored to enhance your understanding of advanced Spark engineering concepts, preparing you for real-world applications and professional certifications.

Multiple-Choice Questions (MCQs)

Partitioning and Shuffling Mechanics

1. What is the primary goal of partitioning in Spark?
   a) Reducing network overhead
   b) Increasing job complexity
   c) Improving data redundancy
   d) Slowing down execution
2. Which operation in Spark typically triggers a shuffle?
   a) Filter
   b) Map
   c) GroupByKey
   d) FlatMap
3. What is the purpose of Spark’s coalesce function?
   a) Reduce the number of partitions
   b) Increase the number of partitions
   c) Eliminate data redundancy
   d) Balance partition sizes
4. What does the term “shuffle” refer to in Spark?
   a) Rearranging data across executors
   b) Loading data into memory
   c) Optimizing file storage
   d) Caching intermediate results
5. Which configuration can be tuned to optimize shuffle performance?
   a) spark.sql.autoBroadcastJoinThreshold
   b) spark.shuffle.compress
   c) spark.executor.memory
   d) spark.driver.port

Optimizing Joins and Aggregations

6. What is a broadcast join in Spark?
   a) Joining datasets by broadcasting a smaller table to all nodes
   b) Performing distributed joins across executors
   c) Joining datasets using shuffle partitions
   d) Merging two large datasets without partitioning
7. Which join type performs best when one dataset is significantly smaller than the other?
   a) Inner join
   b) Broadcast join
   c) Full outer join
   d) Cross join
8. How can you optimize aggregations in Spark?
   a) Use map-side combiners
   b) Increase the shuffle partitions
   c) Reduce executor memory
   d) Enable checkpointing
9. What is the primary use of the reduceByKey transformation in Spark?
   a) Perform aggregations on paired data
   b) Sort data by key
   c) Perform map-side shuffles
   d) Cache intermediate results
10. What is the advantage of using DataFrame APIs over RDDs for aggregations?
    a) Enhanced fault tolerance
    b) Simplified syntax and optimizations
    c) Support for Java integration
    d) Better integration with Hadoop

Custom Serialization and Broadcast Variables

11. Which serialization library does Spark use by default?
    a) JSON
    b) Kryo
    c) Avro
    d) Java Serialization
12. How can you improve performance using Kryo serialization in Spark?
    a) Register custom classes
    b) Disable shuffle compression
    c) Increase executor cores
    d) Reduce partition sizes
13. What are broadcast variables in Spark?
    a) Variables shared across executors for efficient operations
    b) Functions that optimize shuffle operations
    c) Data stored in HDFS for long-term use
    d) Configurations for tuning Spark jobs
14. When should broadcast variables be used in Spark?
    a) When sharing small, read-only datasets across nodes
    b) When reducing shuffle operations
    c) For caching intermediate data
    d) For increasing partition sizes
15. How do you create a broadcast variable in Spark?
    a) SparkContext.broadcast()
    b) DataFrame.broadcast()
    c) SparkSession.cache()
    d) RDD.persist()

Understanding and Avoiding Data Skews

16. What is a common cause of data skew in Spark?
    a) Uneven distribution of data across partitions
    b) Insufficient executor memory
    c) Excessive shuffle operations
    d) High partition counts
17. Which of the following helps mitigate data skews in Spark?
    a) Salting keys
    b) Increasing the shuffle partitions
    c) Disabling caching
    d) Using fewer partitions
18. What happens when a partition contains too much data in Spark?
    a) Task failures and potential out-of-memory errors
    b) Increased shuffle write performance
    c) Reduced execution time
    d) Balanced resource allocation
19. How does salting keys reduce data skews?
    a) By distributing keys more evenly across partitions
    b) By compressing shuffle data
    c) By caching intermediate results
    d) By reducing the number of tasks
20. What metric in the Spark UI can help identify data skews?
    a) Task duration for each stage
    b) Shuffle read and write size
    c) Executor memory usage
    d) Number of jobs executed

Debugging and Profiling Spark Jobs

21. How can you debug a Spark job with failed tasks?
    a) Check the logs in the Spark UI
    b) Increase partition sizes
    c) Enable caching
    d) Reduce executor cores
22. What tool is used to profile Spark job execution?
    a) Ganglia
    b) Spark UI
    c) HDFS Dashboard
    d) YARN Resource Manager
23. Which Spark configuration enables detailed logs for troubleshooting?
    a) spark.eventLog.enabled
    b) spark.executor.instances
    c) spark.shuffle.service.enabled
    d) spark.sql.queryCache.enabled
24. How can you identify slow tasks in Spark?
    a) Analyze the “Tasks” tab in the Spark UI
    b) Check for memory leaks
    c) Monitor Hadoop logs
    d) Review DataFrame schemas
25. What is the purpose of the “Event Timeline” in the Spark UI?
    a) Visualizing the execution stages of a job
    b) Managing executor resources
    c) Configuring Spark session parameters
    d) Optimizing query execution plans
26. What causes a “stage retry” in Spark?
    a) Task failures in the stage
    b) Insufficient shuffle partitions
    c) Large number of executors
    d) Incorrect schema definitions
27. Which command can be used to collect and display job metrics in Spark?
    a) spark-submit --verbose
    b) spark.collectMetrics()
    c) SparkContext.metrics()
    d) RDD.debugInfo()
28. How can you optimize task execution in Spark?
    a) Use smaller partition sizes
    b) Increase executor memory and cores
    c) Disable fault tolerance
    d) Reduce DataFrame caching
29. What is the function of the DAG Scheduler in Spark?
    a) Building and managing the Directed Acyclic Graph (DAG) of tasks
    b) Optimizing query execution plans
    c) Storing metadata for Spark jobs
    d) Handling input and output bindings
30. How can you ensure reproducibility of Spark jobs?
    a) Set random seeds for algorithms and partitions
    b) Use dynamic resource allocation
    c) Increase partition counts
    d) Enable shuffle file compression

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