Perform Logging and Monitoring of Spark Applications - Publish, Customize, and Analyze Driver Logs and Executor Logs to Diagnose Out-Of-Memory Errors, Cluster Underutilization, Etc. ASSOCIATE-DEVELOPER-APACHE-SPARK Practice Questions (Page 6)

Questions 26–30

1. 26application · medium

   A Spark application is running slower than expected. The driver logs show that all stages are being submitted and completed, but there are long gaps between the completion of one stage and the submission of the next. The Spark UI shows no task failures or skew. What is the most likely cause of these gaps?

   • AThe network between the driver and the executors is slow, causing delays in task result serialization.
   • BThe driver is spending time on non-Spark operations, such as fetching data from an external API or performing complex transformations on the driver side.
   • CThe executors are running out of memory and are garbage collecting frequently, causing delays in task completion.
   • DThe cluster is underutilized, and the scheduler is waiting for more resources to be allocated.
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2. 27foundation · easy

   What is the primary purpose of correlating executor logs with Spark UI metrics?

   • ATo verify that the application is using the correct Spark version
   • BTo identify which tasks are slow and why
   • CTo ensure that all executors are using the same JVM settings
   • DTo check if the cluster has enough nodes
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4. 28foundation · easy

   What is the primary role of log4j in a Spark application?

   • ATo schedule tasks across cluster nodes
   • BTo serialize task results between executors and the driver
   • CTo provide the logging framework used by Spark to generate driver and executor logs
   • DTo manage the Spark execution engine's memory allocation
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5. 29expert · hard

   A team is running a Spark job that processes a large dataset. The driver logs show a warning: 'Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources'. The Spark UI shows that the executors are registered and have memory available. What is the most likely cause of this warning?

   • AThe executors do not have enough CPU cores to run the tasks, even though they have memory available.
   • BThe Spark application is in a 'waiting' state because the driver is waiting for user input.
   • CThe data is too large to fit in the executor memory, causing the scheduler to wait for more memory.
   • DThe driver is not able to connect to the executors due to a network issue.
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6. 30foundation · easy

   To reduce the verbosity of Spark's own INFO messages while keeping WARN and ERROR messages, which log4j level should be set for the root logger?

   • AINFO
   • BERROR
   • CWARN
   • DDEBUG
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