SOL-C01 Question #135: Real Exam Question with Answer & Explanation

Question

A data engineering team is tasked with loading a large dataset (5TB) into Snowflake from an external S3 bucket. The data loading process is experiencing significant performance bottlenecks. Which of the following strategies would MOST effectively improve the data loading performance, assuming the network bandwidth between Snowflake and S3 is sufficient?

Options

• AIncrease the size of the virtual warehouse to a larger size (e.g., from SMALL to LARGE) before
• BUse multiple virtual warehouses concurrently to load different subsets of the data from S3.
• CUse a larger virtual warehouse indefinitely to handle any potential performance peaks, even after
• DDisable auto-suspend on the virtual warehouse to prevent it from idling during the data load.
• EPartition the data in S3 into smaller files and ensure the virtual warehouse is appropriately sized

Explanation

Options A and E are the most effective. Increasing warehouse size provides more compute resources for parallel loading. Partitioning the data into smaller files in S3 allows Snowflake to parallelize the load process across multiple compute nodes within the warehouse. Option B might seem useful but Snowflake inherently parallelizes loading from S3 with a single warehouse if the data is properly partitioned. Option C is not cost-effective. Option D might help avoid some overhead but is less impactful than warehouse sizing and data partitioning.

No community discussion yet for this question.