You need to design your Airflow DAG for data quality checks to be scalable and manageable as the number of datasets and checks grows. How can you achieve this?
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?
Discuss the trade-offs between using wide tables (many columns) and narrow tables (few columns) in Spark and the implications for data processing efficiency.
Which operator or feature in Apache Airflow can be used to dynamically adjust the schedule of data quality checks based on the volume of incoming data?
You need to optimize the performance of a Spark query that involves joining data from multiple Hive tables. What strategies can you employ to improve efficiency?
You want to perform an Iceberg table join in CDP using Spark SQL, but you notice it's much slower than expected. What could be some of the reasons? (Choose two)
You are deploying a Spark application on Kubernetes, which requires access to a web I-Jl. You decide to expose it using a Kubernetes service. Which type of service would you typically use to expose the Spark IJI to external traffic?
Describe how you would implement a data lineage solution within the Cloudera Data Engineering service to track the origin and flow of data throughout your data pipelines.
You're developing a Spark application with multiple stages, and you want to ensure that later stages only start processing after all data from the previous stage is complete. How can you achieve this dependency management in Spark?
What does setting the Spark configuration parameter 'spark.sql.shuffle.partitions' impact? A The default level of parallelism for joins and aggregations
