Snowflake SnowPro Certification Practice Test

What are Clustering Keys used for in Snowflake?

1. To partition data for storage

2. To aggregate data during loading

3. To cluster data in a table according to columns/expressions

4. To index data for faster retrieval

The correct answer is: To cluster data in a table according to columns/expressions

Clustering Keys in Snowflake are primarily used to cluster data in a table according to specific columns or expressions. This means that when a clustering key is defined, Snowflake organizes and maintains the data in a way that optimizes query performance for use cases where specific columns are often queried together. By clustering the data based on key columns, Snowflake can reduce the amount of data scanned during queries, leading to improved performance and efficiency. This is particularly beneficial for large datasets where certain patterns of access are common. For example, if a table is frequently queried based on a date column, specifying that column as a clustering key can help Snowflake store the data in such a way that date-based queries can be processed more quickly. Partitioning data for storage is a different concept, as it deals with how data is physically stored but does not relate to the logical organization of data for optimal retrieval in queries. While aggregation can occur during data loading, that's not the function of clustering keys. Indexing is a method common in many databases for fast retrieval of records, but Snowflake utilizes a different architecture that relies more on micro-partitions and clustering keys rather than traditional indexing. Therefore, the correct choice highlights the role of clustering keys in enhancing query performance through strategic data