Implement Performance Tuning Strategies & Optimize Cluster Utilization, Including Partitioning, Repartitioning, Coalescing, Identifying Data Skew, and Reducing Shuffling ASSOCIATE-DEVELOPER-APACHE-SPARK Practice Questions (Page 3)

Questions 11–15

1. 11foundation · easy

   What is the purpose of tuning the spark.sql.shuffle.partitions configuration?

   • ATo enable or disable the broadcast join optimization
   • BTo set the maximum memory usage for each executor
   • CTo specify the number of cores per executor
   • DTo control the number of partitions used during shuffle operations
   Reveal Solution Discussion Select an answer first
2. 12foundation · easy

   How does the partition count of a DataFrame affect job performance in Spark?

   • AThe partition count only affects memory usage, not performance
   • BThe partition count determines the level of parallelism, but too many or too few partitions can both cause inefficiencies
   • CA higher partition count always results in faster job completion because more tasks are created
   • DThe partition count has no impact on performance because Spark automatically optimizes it
   Reveal Solution Discussion Select an answer first
4. 13foundation · easy

   Which technique can help reduce shuffle overhead in Spark?

   • AIncreasing the number of partitions to reduce data size per partition
   • BUsing a broadcast join when one of the tables is small
   • CDisabling the shuffle service in the cluster configuration
   • DUsing map() instead of flatMap() to process data
   Reveal Solution Discussion Select an answer first
5. 14foundation · easy

   Which symptom in the Spark UI is a strong indicator of data skew?

   • AThe job fails with an out-of-memory error on all executors simultaneously
   • BThe number of tasks is much higher than the number of cores in the cluster
   • CAll tasks have similar durations and process similar amounts of data
   • DA few tasks take significantly longer and process much more data than the majority of tasks
   Reveal Solution Discussion Select an answer first
6. 15foundation · easy

   In Apache Spark, what is the primary role of partitioning data across executors?

   • ATo sort the data globally so that queries can return ordered results faster
   • BTo compress the data to reduce storage and memory usage
   • CTo ensure each executor stores a copy of the entire dataset for fault tolerance
   • DTo divide the dataset into smaller chunks so that tasks can be processed in parallel across executors
   Reveal Solution Discussion Select an answer first