最新なCloudera CDP-3002問題集（320題）、真実試験の問題を全部にカバー！

質問 1：
If you want to set a minimum and maximum number of Executor pods for a Spark application in Kubernetes, which pair of PySpark configuration settings would you use?
A. 'spark.executor.memory', 'spark.executor.memoryoverhead'
B. 'spark.executor.instances', 'spark.executor.cores'
C. 'spark.kubernetes.container.image', 'spark.kubernetes.executor.limit.cores'
D. 'spark.dynamicAllocation.minExecutors', 'spark.dynamicAllocation.maxExecutors'
正解：D
解説: (Pass4Test メンバーにのみ表示されます)

質問 2：
In the context of packaging a PySpark application, what is the purpose of the 'requirements.txt' file?
A. To define the Spark version compatible with the application.
B. To list all the third-party dependencies required by the application.
C. To list the environment variables needed for the application.
D. To specify the Python version required for the application.
正解：B
解説: (Pass4Test メンバーにのみ表示されます)

質問 3：
You're working with a large dataset containing nested JSON structures. How can you efficiently process this data using Spark, ensuring data integrity and avoiding excessive parsing overhead?
A. Convert the entire dataset to a single string and process it line by line
B. Use generic string manipulation functions to extract data from JSON
C. Leverage Spark SQL's built-in JSON support with appropriate schema definition
D. Implement a custom parser for the specific JSON structure
正解：C
解説: (Pass4Test メンバーにのみ表示されます)

質問 4：
How can you use Apache Airflow to ensure a data quality check stops the workflow if it fails, without failing subsequent tasks that are not dependent on the data quality check?
A. Set trigger_rule='all_done' on subsequent tasks.
B. Use the ShortCircuitOperator with a condition that returns False if the check fails.
C. Use the TaskGroup with trigger_rule='one_failed' for the data quality check task.
D. Use the BranchPythonOperator to split the workflow conditionally.
正解：B
解説: (Pass4Test メンバーにのみ表示されます)

質問 5：
What command and parameters should be used to update an existing Spark job in the Cloudera Data Engineering (CDE. service to increase its executor memory using the CDE CLI?
A. cde job config -name my-spark-job -set spark.executor.memory=6g
B. cde job update -name my-spark-job --conf spark.executor.memory=6g
C. cde job run -edit my-spark-job -conf spark.executor.memory=6g
D. cde spark submit -update -name my-spark-job -executor-memory 6G
正解：B
解説: (Pass4Test メンバーにのみ表示されます)

質問 6：
How does Spark handle data shuffling during distributed processing?
A. By transferring only required data between executors
B. By storing all data on a single node
C. By broadcasting all data to each executor
D. Spark doesn't perform data shuffling
正解：A
解説: (Pass4Test メンバーにのみ表示されます)

質問 7：
What is the best practice for handling DAG dependencies in Apache Airflow when one DAG's output is another DAG's input?
A. Manually trigger the dependent DAG after the first DAG completes.
B. Use the ExternalTaskSensor to wait for a task in another DAG to complete.
C. Directly call one DAG from another using the PythonOperator.
D. Use the TriggerDagRunOperator to trigger one DAG from another upon completion.
正解：B
解説: (Pass4Test メンバーにのみ表示されます)

質問 8：
In a Kubernetes environment, you want to restrict the communication to your Spark application pods to only allow traffic from pods in a specific namespace. Which Kubernetes feature would you use to implement this?
A. Network Policies
B. StatefulSets
C. Deployments
D. Service Mesh
正解：A
解説: (Pass4Test メンバーにのみ表示されます)