Snowflake SnowPro Certification Practice Test

What is one requirement for Clustering Keys to improve query performance in Snowflake?

1. The table must be small

2. Clustering Keys must provide no filtering

3. The table must have many micro-partitions

4. Clustering Keys must be consistent

The correct answer is: The table must have many micro-partitions

To effectively utilize clustering keys in Snowflake and enhance query performance, there is a fundamental relationship between clustering and data partitioning. Clustering keys are designed to organize data within micro-partitions in a manner that optimizes query performance, particularly for large datasets. When a table has many micro-partitions, there is an increased opportunity for Snowflake to efficiently prune irrelevant partitions during query execution. This micro-partitioning mechanism allows Snowflake to quickly access only those parts of the data that are pertinent to the query, rather than scanning through the entire dataset. As a result, having many micro-partitions can lead to faster data retrieval times when the data is clustered effectively. This concept emphasizes the importance of partitioning when dealing with substantial datasets: the more micro-partitions you have, the more selective Snowflake can be in fetching data, thus speeding up query performance. In contrast, if there aren’t enough micro-partitions, the advantages of clustering keys can diminish significantly, as the potential filtering benefits are not realized. Therefore, the requisite for a table to have many micro-partitions is crucial for clustering keys to be effective in improving query performance.