This article introduces FinCDM, a framework for assessing financial large language models (LLMs) by evaluating their knowledge and skills rather than relying on a single score. It highlights the creation of a new dataset, CPA-KQA, based on CPA exam questions, which allows for a more nuanced analysis of LLM capabilities in financial contexts. The framework aims to uncover knowledge gaps and enhance model development for real-world applications.