In Snowflake, what factors determine the effectiveness of using materialized views for query optimization?

Question

Options

• ALimitations in accessing historical data
• BCompatibility with specific BI tools only
• CQuery result caching capabilities
• DFrequency of data updates and refresh requirements

• A
  11% (6)
• B
  14% (8)
• C
  75% (42)

Explanation

Materialized views in Snowflake derive their optimization power from two core mechanisms: they physically store pre-computed query results (making them a form of persistent caching, C), and their value is directly tied to how often the underlying data changes (D) - frequent updates force costly refreshes that can erode performance gains or leave views temporarily stale. Together, these factors determine whether the overhead of maintaining a materialized view is justified by the query speedup it provides.

Why A is wrong: Accessing historical data is a Snowflake Time Travel concern, not a materialized view concern - MVs don't have special limitations there.

Why B is wrong: Materialized views expose standard SQL interfaces and work with any compatible tool, not a subset of BI platforms.

Memory tip: Think of a materialized view as a "frozen snapshot" - its power comes from what it stores (cached results, C) and how often the freezer needs defrosting (refresh frequency, D). If the data changes constantly, the freezer is always defrosting and the snapshot is never useful.

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