When using UDFs within Apache Spark for distributed operations like deep learning model processing, it is common to encounter Spark's "SparkContext can only be used on the driver" problem. This occurs due to the stringent driver-bound nature of SparkContext, which controls job distribution. By preventing serialization conflicts in distributed image processing pipelines and guaranteeing model access without re-initialization on each node, solutions such as broadcast variables enable us to share models with worker nodes in an efficient manner. Spark's capacity to handle intricate machine learning tasks at scale is greatly improved by broadcast approaches.