Optimizing Query Performance in Delta Lake: For a Databricks job that processes terabytes of data daily, which data partitioning strategy would best enhance query performance on the processed data stored in Delta Lake, given diverse query patterns?

Real Exam

Employing a custom partitioner that dynamically adapts partitions according to query workload and access patterns

11.5%

Partitioning data by date, under the assumption that most queries filter based on a date range

20.4%

Not partitioning the data and depending solely on Delta Lake's optimization features, such as Z-ordering, to boost query performance

10.8%

Adopting a range partitioning strategy centered around a column that's frequently queried to ensure uniform data distribution

57.3%

Databricks Certified Data Engineer - Professional