A table for IOT devices that measures water usage is created. The table quickly becomes large and contains more than 2 billion rows. The general query patterns for the table are: 1. DeviceId, lOT_time

Question

Exhibit

Options

• AlOT_timestamp
• BCity and DeviceManuf acturer
• CDeviceld and Customerld
• DUniqueld

• A
  3% (1)
• B
  8% (3)
• C
  75% (30)
• D
  15% (6)

Explanation

Clustering keys should be chosen based on columns most frequently used in filter predicates (WHERE clauses), as they determine how Snowflake organizes micro-partitions for efficient pruning. Per the query patterns, DeviceId, IOT_timestamp, and CustomerId are all used in filters. However, IOT_timestamp is a poor clustering key alone because it is monotonically increasing and high-cardinality, causing poor partition overlap. DeviceId and CustomerId (Option C) represent categorical identifiers with better cardinality characteristics for clustering. Option B (City and DeviceManufacturer) are only retrieved, not filtered. Option D (UniqueId) is used in COUNT aggregation, not filtering, and is too high-cardinality for effective clustering.

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