What do frequent garbage collection (GC) pauses in executor logs suggest?

The executor is experiencing memory pressure, potentially leading to OOM

The executor is performing disk I/O

The executor is waiting for network I/O

A team is troubleshooting a Spark job that is failing. They need to find the driver logs, but they are not sure where to look. They are running the job on a standalone Spark cluster. Where are the driver logs typically located?

In the Spark History Server's event log directory.

In the directory specified by the SPARK_LOG_DIR environment variable, or in the 'logs' directory under the Spark installation directory.

In the YARN container logs, accessible via the YARN ResourceManager UI.

In the executor's working directory on the worker nodes.

How can you correlate a specific log entry with a stage in the Spark UI?

By using the application ID in the log

By comparing timestamps with the UI's timeline

By checking the executor ID in the log against the UI's executor list

By matching the stage ID mentioned in the log with the stage ID shown in the UI

Which executor log message indicates a task failure due to a fetch failure?

org.apache.spark.shuffle.FetchFailedException

java.io.IOException: Connection refused

java.lang.OutOfMemoryError: Java heap space

org.apache.spark.SparkException: Task not serializable

Which approach can be used to publish Spark driver and executor logs to a centralized log aggregator for analysis?

Using the Spark History Server to stream logs in real time

Enabling spark.eventLog.enabled to write logs to HDFS

Setting spark.sql.shuffle.partitions to a higher value

Configuring a log4j appender that writes to a remote syslog or HTTP endpoint

DatabricksCertified Associate Developer for Apache Spark

Domain 4Objective 3

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 5)

Part of the Troubleshooting and Tuning Apache Spark DataFrame API Applications. domain, which accounts for 10% of the ASSOCIATE-DEVELOPER-APACHE-SPARK exam. Databricks does not publish an official question count, but from its 90-minute exam (~35–60 total, ~4–6 in this domain), expect 1–2 from this objective — we provide 35 practice questions to prepare you well beyond it. (estimate)

35questions here

7free pages

10concepts

10%of the exam

10 Concepts in This Objective

Spark logging architecture
Configuring Spark logging
Publishing logs to external systems
Customizing log formats
Analyzing driver logs
Analyzing executor logs
Diagnosing out-of-memory errors
Diagnosing cluster underutilization
Using Spark UI for log correlation
Monitoring application progress

Questions 21–25

21foundation · easy
What do frequent garbage collection (GC) pauses in executor logs suggest?
Select an answer first
22application · medium
A team is troubleshooting a Spark job that is failing. They need to find the driver logs, but they are not sure where to look. They are running the job on a standalone Spark cluster. Where are the driver logs typically located?
Select an answer first
23foundation · easy
How can you correlate a specific log entry with a stage in the Spark UI?
Select an answer first
24foundation · easy
Which executor log message indicates a task failure due to a fetch failure?
Select an answer first
25foundation · easy
Which approach can be used to publish Spark driver and executor logs to a centralized log aggregator for analysis?
Select an answer first

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