Snowflake Certification Practice Test

During pruning in Snowflake, what is the first step?

1. Eliminate columns by sorting

2. Eliminate cluster key entries from query filters

3. Eliminate unnecessary micro-partitions

4. Eliminate by column within micro-partitions

The correct answer is: Eliminate cluster key entries from query filters

In the context of Snowflake's query optimization, pruning is a critical process aimed at enhancing performance by reducing the amount of data processed during query execution. The initial step in this process involves eliminating cluster key entries from query filters. When a query is executed, Snowflake looks at the cluster keys defined for the table to determine if any portion of the data can be disregarded based on the specified filters. If the query filters include values present in the cluster key, Snowflake can quickly identify which micro-partitions are relevant to the query and eliminate those that do not contain the necessary data, thereby efficiently narrowing down the search space. This stems from Snowflake's architecture, where data is organized into micro-partitions. Each micro-partition contains metadata that includes the minimum and maximum values of the columns within it, particularly for any specified cluster keys. As such, before delving into the micro-partitions themselves, it is logical to use the cluster key information to filter out any irrelevant partitions right off the bat. Subsequent steps in the pruning process involve narrowing down on a more granular level by looking at unnecessary micro-partitions and eliminating data at the column level, but the first logical step begins with examining the cluster keys. This prioritization aligns with