最新のCDP-3002問題集で試験を準備する

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

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

A data engineer needs to query a table stored in Apache Hive using SparkSQL. Which of the following commands correctly retrieves data from a Hive table named 'sales data'?

• A.
  
• B.
  
• C.
  
• D.
  

解説: (JPTestKing メンバーにのみ表示されます)

You want to write the results of a Spark DataFrame operation to a Parquet file for efficient storage and retrieval. What approach can you achieve this efficiently?

• A. Use Spark SQL's CREATE TABLE statement with the Parquet format specified
• B. Leverage the DataFrame.write.parquet() method with appropriate options
• C. Use the DataFrame.write.text() method and specify the output format as Parquet
• D. Convert the DataFrame to a temporary table and then use HiveQL commands to write to Parquet

解説: (JPTestKing メンバーにのみ表示されます)

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

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

解説: (JPTestKing メンバーにのみ表示されます)

You're working with a large dataset stored in multiple Parquet files across different HDFS directories. How can you efficiently load and process this data using Spark, ensuring data locality and minimizing shuffle operations?

• A. Use spark.read.parquet("/path/to/data/") with recursive directory listing
• B. Directly load all files using spark.read.parquet("/path/to/data/")
• C. Leverage Spark SQL catalogs and partition discovery
• D. Implement a custom function to read each Parquet file individually

解説: (JPTestKing メンバーにのみ表示されます)