An engineer has a strong theoretical understanding of the various components involved in deploying large, complex computational models, including algorithms and system architecture. However, when tasked with improving the efficiency of an actual deployed system, they find it difficult to identify and resolve performance bottlenecks. What is the most likely reason for this discrepancy between their theoretical knowledge and practical performance, and what does this suggest about the nature of mastering this field?