SnowPro Associate: Platform Question 8
Single answer● Elastic storageA retail company stores three years of sales data in Snowflake. Most analysts query only the last 90 days, but auditors occasionally run reports against older data. The data volume is growing rapidly, and the team wants a solution that scales storage independently from compute while avoiding the need to reorganize files or provision additional disk capacity for warehouses. Which Snowflake capability best addresses this requirement?
- A
Snowflake's elastic storage layer, which automatically scales storage separately from virtual warehouse compute
- B
Increasing the size of the virtual warehouse so that it includes more persistent storage for older data
- C
Using result caching so historical data does not need to be stored after the first query
- D
Converting permanent tables to temporary tables so infrequently accessed historical data consumes fewer storage resources
Show answer and explanation
Correct answer: A
Explanation
This scenario tests understanding of Snowflake's architecture, specifically elastic storage. In Snowflake, storage and compute are decoupled: table data is stored in cloud object storage managed by Snowflake, while virtual warehouses provide independent compute for query processing. This means organizations can store very large and growing datasets without provisioning storage attached to compute clusters. Analysts can use smaller or larger warehouses based on workload, while the underlying storage scales separately. This is a key Snowflake design principle and an important operational benefit for real-world environments with large historical datasets and occasional access patterns. Relevant Snowflake documentation emphasizes the separation of storage, compute, and cloud services layers, and notes that virtual warehouses do not own the stored data.
- A. Correct.
Correct. Snowflake separates storage and compute, allowing data volumes to grow without requiring changes to warehouse size. This is a core benefit of Snowflake's elastic storage model: data is stored centrally in cloud storage and compute resources are provisioned independently through virtual warehouses.
- B. Incorrect.
Incorrect. Virtual warehouses provide compute resources, not persistent table storage capacity. Increasing warehouse size can improve query performance, but it does not add durable storage for table data or solve long-term data growth requirements.
- C. Incorrect.
Incorrect. Result caching can improve performance for repeated identical queries, but it is not a storage management feature and does not eliminate the need to retain historical table data. Cached results are temporary and cannot replace underlying data storage.
- D. Incorrect.
Incorrect. Temporary tables are session-scoped and are not appropriate for long-term historical or audit data retention. They are not a mechanism for reducing storage requirements for permanent business data that must remain accessible over time.