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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q41-Q46):

NEW QUESTION # 41
Given:
python
CopyEdit
spark.sparkContext.setLogLevel("<LOG_LEVEL>")
Which set contains the suitable configuration settings for Spark driver LOG_LEVELs?

  • A. FATAL, NONE, INFO, DEBUG
  • B. ERROR, WARN, TRACE, OFF
  • C. WARN, NONE, ERROR, FATAL
  • D. ALL, DEBUG, FAIL, INFO

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
ThesetLogLevel()method ofSparkContextsets the logging level on the driver, which controls the verbosity of logs emitted during job execution. Supported levels are inherited from log4j and include the following:
ALL
DEBUG
ERROR
FATAL
INFO
OFF
TRACE
WARN
According to official Spark and Databricks documentation:
"Valid log levels include: ALL, DEBUG, ERROR, FATAL, INFO, OFF, TRACE, and WARN." Among the choices provided, only option B (ERROR, WARN, TRACE, OFF) includes four valid log levels and excludes invalid ones like "FAIL" or "NONE".
Reference: Apache Spark API docs # SparkContext.setLogLevel


NEW QUESTION # 42
How can a Spark developer ensure optimal resource utilization when running Spark jobs in Local Mode for testing?
Options:

  • A. Configure the application to run in cluster mode instead of local mode.
  • B. Set the spark.executor.memory property to a large value.
  • C. Increase the number of local threads based on the number of CPU cores.
  • D. Use the spark.dynamicAllocation.enabled property to scale resources dynamically.

Answer: C

Explanation:
When running in local mode (e.g., local[4]), the number inside the brackets defines how many threads Spark will use.
Using local[*] ensures Spark uses all available CPU cores for parallelism.
Example:
spark-submit --masterlocal[*]
Dynamic allocation and executor memory apply to cluster-based deployments, not local mode.
Reference:Spark Master URLs


NEW QUESTION # 43
A Spark developer is building an app to monitor task performance. They need to track the maximum task processing time per worker node and consolidate it on the driver for analysis.
Which technique should be used?

  • A. Use an accumulator to record the maximum time on the driver
  • B. Configure the Spark UI to automatically collect maximum times
  • C. Use an RDD action like reduce() to compute the maximum time
  • D. Broadcast a variable to share the maximum time among workers

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The correct way to aggregate information (e.g., max value) from distributed workers back to the driver is using RDD actions such asreduce()oraggregate().
From the documentation:
"To perform global aggregations on distributed data, actions likereduce()are commonly used to collect summaries such as min/max/avg." Accumulators (Option B) do not support max operations directly and are not intended for such analytics.
Broadcast (Option C) is used to send data to workers, not collect from them.
Spark UI (Option D) is a monitoring tool - not an analytics collection interface.
Final Answer: A


NEW QUESTION # 44
A Spark engineer is troubleshooting a Spark application that has been encountering out-of-memory errors during execution. By reviewing the Spark driver logs, the engineer notices multiple "GC overhead limit exceeded" messages.
Which action should the engineer take to resolve this issue?

  • A. Modify the Spark configuration to disable garbage collection
  • B. Optimize the data processing logic by repartitioning the DataFrame.
  • C. Increase the memory allocated to the Spark Driver.
  • D. Cache large DataFrames to persist them in memory.

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The message"GC overhead limit exceeded"typically indicates that the JVM is spending too much time in garbage collection with little memory recovery. This suggests that the driver or executor is under-provisioned in memory.
The most effective remedy is to increase the driver memory using:
--driver-memory 4g
This is confirmed in Spark's official troubleshooting documentation:
"If you see a lot ofGC overhead limit exceedederrors in the driver logs, it's a sign that the driver is running out of memory."
-Spark Tuning Guide
Why others are incorrect:
Amay help but does not directly address the driver memory shortage.
Bis not a valid action; GC cannot be disabled.
Dincreases memory usage, worsening the problem.


NEW QUESTION # 45
A data engineer wants to write a Spark job that creates a new managed table. If the table already exists, the job should fail and not modify anything.
Which save mode and method should be used?

  • A. saveAsTable with mode Overwrite
  • B. save with mode ErrorIfExists
  • C. saveAsTable with mode ErrorIfExists
  • D. save with mode Ignore

Answer: C

Explanation:
Comprehensive and Detailed Explanation:
The methodsaveAsTable()creates a new table and optionally fails if the table exists.
From Spark documentation:
"The mode 'ErrorIfExists' (default) will throw an error if the table already exists." Thus:
Option A is correct.
Option B (Overwrite) would overwrite existing data - not acceptable here.
Option C and D usesave(), which doesn't create a managed table with metadata in the metastore.
Final Answer: A


NEW QUESTION # 46
......

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