In a lakehouse architecture requiring frequent and complex aggregations (e.g., rolling averages, cumulative sums) across large datasets, which strategy ensures the most efficient query performance?

Real Exam

Dynamically compute aggregations on-the-fly using Spark‘s advanced analytics functions, caching results where possible.

11.5%

Create materialized views that automatically refresh at predefined intervals, storing intermediate aggregation results.

28.7%

Leverage a combination of Delta Lake‘s time travel feature and Spark‘s window functions to compute and store aggregations incrementally.

41.8%

Precompute and store all possible aggregations in separate Delta tables, updating them incrementally.

18.0%

Databricks Certified Data Engineer - Professional