SOL-C01 · Question #114
You are responsible for optimizing data loading into Snowflake from a large set of CSV files stored in Azure Blob Storage. The files contain millions of records and are updated daily You need to choos
The correct answer is A. Use Snowpipe with auto-ingest enabled and a dedicated X-SMALL virtual warehouse. D. Implement Snowpipe with a notification integration that triggers data loading only when new files. Snowpipe with auto-ingest (A) and notification-triggered ingestion (D) form the optimal pairing because they both leverage Snowflake's event-driven loading architecture, which processes files as they arrive rather than on a fixed schedule - eliminating idle compute time and reduc
Question
You are responsible for optimizing data loading into Snowflake from a large set of CSV files stored in Azure Blob Storage. The files contain millions of records and are updated daily You need to choose the MOST efficient method for loading this data, considering both performance and cost. Select TWO options that would best address this scenario.
Options
- AUse Snowpipe with auto-ingest enabled and a dedicated X-SMALL virtual warehouse.
- BUse a scheduled task that executes a COPY INTO statement with a large virtual warehouse (e.g.,
- CUtilize Snowflake's Data Marketplace to subscribe to a pre-built data feed containing similar data.
- DImplement Snowpipe with a notification integration that triggers data loading only when new files
- EUse the Snowflake web IJI to manually upload the CSV files on a daily basis.
How the community answered
(31 responses)- A65% (20)
- B6% (2)
- C23% (7)
- E6% (2)
Explanation
Snowpipe with auto-ingest (A) and notification-triggered ingestion (D) form the optimal pairing because they both leverage Snowflake's event-driven loading architecture, which processes files as they arrive rather than on a fixed schedule - eliminating idle compute time and reducing cost. Snowpipe internally manages micro-batches, making an X-SMALL warehouse sufficient and cost-appropriate; you're charged only for actual ingestion compute. Option D reinforces this by using a notification integration (e.g., Azure Event Grid) to fire Snowpipe only when new files land, ensuring zero wasted processing on unchanged data.
Why the distractors are wrong:
- B wastes money by spinning up a large warehouse on a schedule, even if no new files exist or the data volume doesn't justify it.
- C is a red herring - the Data Marketplace provides access to third-party datasets; it cannot ingest your own CSV files from Azure Blob Storage.
- E is a manual process that doesn't scale to millions of records and defeats the purpose of automation entirely.
Memory tip: Think of Snowpipe as a "motion sensor light" - it only turns on (loads data) when something moves (new file arrives), and it uses a small bulb (X-SMALL warehouse) because micro-batches don't need brute-force compute. If you're scheduling bulk loads with a giant warehouse, you've left the lights on all night.
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