Answer: Apply Databricks SQL Query filters to reduce data in visualizations

✅ **C. Apply Databricks SQL Query filters to reduce data in visualizations** This approach directly tackles the issue by limiting the data processed for each visualization, leading to faster loading times. Filters enhance performance by reducing query execution and rendering times, and they improve user experience by focusing on relevant data subsets. ❌ **A. Expand the maximum scaling range of the SQL endpoint cluster** While this allows the cluster to handle more concurrent queries, it doesn't directly reduce the data volume for individual visualizations and may increase costs. ❌ **B. Utilize Delta cache for storing intermediate results** Delta cache speeds up repeated data access but doesn't help with the initial load of large data volumes in visualizations. ❌ **D. Enlarge the SQL endpoint cluster size** Increasing cluster size can improve query performance but is a less targeted and more costly solution compared to data filtering. ❌ **E. Delete data from Delta Lake** Removing data can reduce storage costs and speed up some queries, but it's a broad approach that may lead to loss of valuable information and doesn't specifically address visualization loading issues.

Author: LeetQuiz Editorial Team