Cloud data lakes are a modern way to manage vast amounts of data. They separate the compute and storage layers, which makes them highly scalable and cost-effective. However, query performance in cloud data lakes is relatively slow, and many attempts have been made to improve it in recent years. In this paper, we introduce our approach to this problem based on a novel caching technique. Our method relies on the observation that a significant bottleneck in query performance is caused by reading irrelevant files from remote storage. To address this, we suggest caching the relevant file names per query and reusing them in subsequent queries contained in some of the cached queries. Our solution uses ideas from both databases and computational geometry fields. Experiments based on the TPC-H benchmark demonstrate its feasibility and efficiency.