CDP-3002 試験問題を無料オンラインアクセス

For a Hive table that is both partitioned and bucketed, what considerations must be taken into account to optimize a join query involving this table?

• A.Ensuring the join columns are neither partitioned nor bucketed as it may lead to increased complexity.
• B.Both the partitioning and bucketing columns should align with the join columns where possible to maximize the efficiency of data retrieval.
• C.Bucketing considerations are irrelevant in the context of join queries, with partitioning being the sole factor impacting performance.
• D.The join should exclusively rely on the partitioned columns, ignoring the bucketed columns for optimal performance.

In Apache Airflow, which operator is best suited for running data quality checks on a Hive table after data ingestion?

• A.BashOperator
• B.PythonOperator
• C.HiveOperator
• D.ShortCircuitOperator

You're deploying your Airflow DAGs to a production environment. What are some best practices to ensure reliability and maintainability?

• A.Grant full access to the Airflow web UI to everyone for monitoring and troubleshooting purposes.
• B.Use the same Airflow environment for development, testing, and production deployments.
• C.Implement version control for your DAG code and configuration files to track changes and facilitate rollbacks.
• D.Schedule DAGs to run as frequently as possible to ensure real-time data processing.

You need to handle potential errors and retries within your Airflow ETL pipeline. How can you achieve this functionality?

• A.Implement exception handling within your custom Python operators and define retry behavior manually.
• B.All of the above
• C.Use the BranchPythonOperator to check for specific error conditions and branch the DAG execution accordingly.
• D.Configure the DAG to retry failed tasks automatically with a set number of retries.

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.kubernetes.container.image', 'spark.kubernetes.executor.limit.cores'
• B.'spark.dynamicAllocation.minExecutors', 'spark.dynamicAllocation.maxExecutors'
• C.'spark.executor.memory', 'spark.executor.memoryoverhead'
• D.'spark.executor.instances', 'spark.executor.cores'