ADA-C01 Question 418
Single answerUse WAREHOUSE_MONITORING to optimize costsA Snowflake administrator is investigating unexpectedly high warehouse spend in an account with many analyst-owned virtual warehouses. The administrator wants a built-in way to identify warehouses that are consuming credits inefficiently so they can target tuning efforts, such as reducing unnecessary warehouse size or adjusting auto-suspend settings. Which approach best uses WAREHOUSE_MONITORING to optimize costs?
- A
Query the WAREHOUSE_MONITORING view to compare credit consumption and utilization patterns across warehouses, then focus on warehouses showing high credit usage with low effective utilization or excessive idle time
- B
Use WAREHOUSE_MONITORING to automatically resize all warehouses to X-SMALL whenever daily credit consumption exceeds a threshold
- C
Use WAREHOUSE_MONITORING to identify warehouses with the highest number of queries, because query count alone is the most reliable indicator of warehouse cost inefficiency
- D
Query WAREHOUSE_MONITORING to find warehouses with long-running queries only, because warehouse cost optimization is limited to query duration analysis
Show answer and explanation
Correct answer: A
Explanation
The best answer is to use WAREHOUSE_MONITORING as an observability source to compare warehouse cost and activity patterns, then target the warehouses where credits are being spent inefficiently. In real Snowflake administration, warehouse cost optimization commonly involves identifying overprovisioned warehouses, warehouses with poor auto-suspend settings, and warehouses that consume credits without corresponding productive workload. Monitoring data should support evidence-based tuning rather than blanket resizing or simplistic metrics like query count alone. This aligns with Snowflake best practices around warehouse monitoring, right-sizing, and minimizing idle warehouse runtime.
- A. Correct.
Correct. WAREHOUSE_MONITORING is intended to help administrators analyze warehouse behavior and spending patterns so they can identify inefficiencies. In practice, cost optimization involves correlating warehouse credit consumption with warehouse activity and utilization characteristics, such as overprovisioning, poor auto-suspend settings, and periods where warehouses remain running without doing useful work. This is the most direct and practical use of warehouse monitoring data for targeted cost reduction.
- B. Incorrect.
Incorrect. WAREHOUSE_MONITORING provides observability data; it does not automatically resize warehouses based on thresholds. Also, resizing every warehouse to X-SMALL would be an unsafe blanket action because workload requirements differ. Cost optimization should be based on observed utilization and workload characteristics, not a one-size-fits-all automation assumption.
- C. Incorrect.
Incorrect. Query count by itself is not a reliable measure of cost inefficiency. A warehouse can process many short, efficient queries at low cost, while another warehouse may process fewer but much more expensive queries or remain idle while running. The misconception is equating volume of queries with inefficient spend without considering credits consumed, runtime, concurrency, and idle behavior.
- D. Incorrect.
Incorrect. Long-running queries can contribute to cost, but warehouse cost optimization is broader than query-duration analysis. WAREHOUSE_MONITORING is useful for identifying overall warehouse consumption patterns, including over-sized warehouses and warehouses left running while inactive. Focusing only on long-running queries would miss common cost drivers such as idle time and poor sizing.