While printing statements A might provide some insights, relying solely on logs B limits visibility. Spark's Lineage Viewer C offers a visual representation of data flow, helping pinpoint where issues might occur. Additionally, unit testing individual Spark operations D can isolate and identify problems within specific code sections.

The 'requirements.txt' file is used to list all third-party libraries (dependencies) that the PySpark application needs. These dependencies are then installed using pip.

A DAG (Directed Acyclic Graph) in Apache Airflow represents a collection of tasks organized to reflect their relationships and dependencies. It is not a programming language, an operator, or a visualization tool; it's a concept that defines the structure of a workflow.

Setting hive.exec.dynamic.partition.mode to nonstrict in Hive configuration allows for the creation of new partitions dynamically based on the input data values during data insertion operations. This mode removes the restriction that requires at least one static partition to be specified in the query, thereby providing greater flexibility in managing and organizing data into partitions based on the actual values encountered at runtime.

"Out of memory" often means the driver or executors lack sufficient RAM. C, D might help in specific cases, but the primary solution is more memory. E is valid but assumes your cluster has scaling capacity.