Databricks Machine Learning Professional Question 158
Single answerYou are working with a large dataset in Delta Lake stored on Databricks, and your machine learning pipeline frequently queries the dataset for training data based on the 'region' column. You notice that the queries on this column are slow. How can you optimize the query performance in this scenario?
- A
Partition the dataset on the 'region' column to enable faster lookups.
- B
Use Delta Lake Z-Ordering on the 'region' column to co-locate relevant data.
- C
Cache the entire dataset in memory to avoid disk I/O during queries.
- D
Increase the cluster size to improve query execution speed.
Show answer and explanation
Correct answer: A
Explanation
Partitioning on the 'region' column is the most effective strategy in this scenario because it aligns the physical data organization with the query pattern, allowing Databricks to skip irrelevant partitions and read only the necessary data. This approach directly addresses the performance issue by reducing the amount of data scanned during queries.
- A. Correct.
Partitioning the dataset on the 'region' column creates separate physical storage for each region, enabling selective reading of only the relevant partitions during queries. This significantly reduces query latency.
- B. Incorrect.
Z-Ordering optimizes data layout on disk for multi-dimensional queries but is less effective than partitioning when queries are consistently filtering on a single column like 'region'.
- C. Incorrect.
Caching the dataset in memory can improve performance, but it is not a sustainable solution for extremely large datasets as memory resources are limited.
- D. Incorrect.
Increasing the cluster size can improve performance in some cases, but it does not directly address the root cause of slow query performance related to data layout on disk.