For a global-scale analytics platform built on a lakehouse, which data partitioning strategy is most effective in reducing query latency for teams spread across different geographical locations?

Real Exam

Use hash partitioning on a globally unique identifier to ensure even distribution of data across partitions, regardless of geographic location.

11.3%

Implement range partitioning based on timestamp to optimize for temporal queries common in analytics workloads.

5.0%

Partition data by country or region and replicate partitions across global data centers to localize data access.

73.8%

Rely on the lakehouse‘s default partitioning mechanism, focusing on optimizing network connectivity between regions instead.

10.0%

Databricks Certified Data Engineer - Professional