COF-C03 Data Loading, Unloading, and Connectivity Practice Question
For optimal parallel loading performance using a Snowflake virtual warehouse, what is the generally recommended compressed file size range for data files in a stage?
⚠ Common exam trap
Candidates often confuse the recommended file size range for standard data loading (10MB to 100MB compressed) with larger bulk loading recommendations or uncompressed file sizes.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
10MB to 100MB
Snowflake's architecture is optimized for parallel processing, where each execution thread in a warehouse can process a separate file. To maximize this parallelism and avoid overhead, it is recommended to aim for file sizes between 10MB and 100MB when compressed. This ensures that the workload is distributed evenly across all available compute nodes without overwhelming the system with metadata management.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
1KB to 100KB
Why it's wrong here
Loading very small files (kilobyte range) is highly inefficient because it creates significant overhead in the Snowflake metadata layer. Each file requires a separate request and tracking entry, which can lead to longer load times and 'file congestion'. It is much better to aggregate these small files into larger batches before attempting to load them.
- ✓
10MB to 100MB
Why this is correct
The 10MB to 100MB range is the 'sweet spot' for Snowflake's data ingestion engine. This size allows for efficient distribution of files across the CPUs in a virtual warehouse. It balances the need for parallelism with the need to minimize the number of files the system must track, resulting in the fastest possible bulk loading performance.
- ✗
1GB to 5GB
Why it's wrong here
While Snowflake can handle large files, very large files (gigabyte range) can reduce parallelism. A single large file can only be processed by one thread at a time, potentially leaving other cores in the warehouse idle. This leads to longer overall load times compared to a scenario where the same data is split into multiple smaller files.
- ✗
Exactly 256MB to match HDFS blocks
Why it's wrong here
While 256MB is a common block size in Hadoop/HDFS environments, it is not the specific recommendation for Snowflake. Snowflake's internal micro-partitions and ingestion engine are optimized differently. Sticking to the 10-100MB range provides better flexibility and performance across different warehouse sizes, from X-Small to the largest available sizes.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Snowflake exam blueprint
This COF-C03 practice question is part of Courseiva's free Snowflake certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the COF-C03 exam.