You are managing a Snowflake environment that ingests data from various sources, including structured data (CSV files) and semi-structured data (JSON files). You notice that query performance is…

Question

Options

• AYou suspect that inefficient data loading practices and suboptimal virtual warehouse configurations
• BConsistently use the smallest virtual warehouse size possible for all data loading tasks to minimize
• CImplement micro-batching for data loading, breaking down large files into smaller chunks for
• DOptimize the virtual warehouse sizing for different workloads, using larger warehouses for complex
• ERegularly analyze query performance using Snowflake's Query Profile and identify areas for
• FDisable automatic query optimization to have more control over query execution plans.

• A
  13% (3)
• B
  57% (13)
• E
  26% (6)
• F
  4% (1)

Explanation

Important caveat first: This question has a significant internal contradiction - option B ("always use the smallest warehouse") directly conflicts with option D ("optimize warehouse sizing for different workloads, using larger warehouses for complex [queries]"). A well-formed question would not have both as correct. With that noted, here's how the exam likely intends them:

Why B, C, D are marked correct: The intended reasoning is that right-sizing virtual warehouses per workload (D) improves performance, where simple bulk data loads can often use smaller warehouses (B) while complex transformations use larger ones. Micro-batching (C) is a genuine Snowflake best practice - loading smaller file chunks (100–250 MB) improves parallelism, avoids file spillover, and speeds ingestion for both CSV and JSON sources.

Why the distractors are wrong: Option A only identifies the problem without prescribing any corrective action, making it a diagnosis rather than a solution. Option E (Query Profile analysis) is genuinely good practice but is treated here as a diagnostic tool rather than an optimization strategy - and the question asks for optimization actions. Option F is clearly wrong: disabling Snowflake's automatic query optimization removes its cost-based planner, which nearly always worsens performance.

Memory tip: Think "Right-size, Batch-smart, Don't disable" - match warehouse size to workload complexity, break large files into micro-batches, and never turn off Snowflake's built-in optimizer.

Flag this question if encountered on a real exam - B and D are contradictory, and E (Query Profile) is arguably more actionable than B.

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