COF-C03 Data Loading, Unloading, and Connectivity Practice Question
A data engineer is loading a 4 GB CSV file from an external stage into a Snowflake table using COPY INTO. The file is compressed with gzip and has a header row. The engineer notices the load is taking longer than expected. Which action is MOST likely to improve performance?
⚠ Common exam trap
The trap here is assuming that increasing warehouse size alone will always speed up a single large file load, but Snowflake cannot parallelize within a single file.
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
✓
Split the file into multiple smaller files and load them in parallel.
Splitting large files into multiple smaller files enables parallel loading, which is a key performance optimization for COPY INTO. Snowflake can distribute the load across multiple threads when multiple files are present, reducing overall load time.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable the PURGE option to remove the file after loading.
Why it's wrong here
Enabling PURGE=TRUE removes the source file after a successful load, which can save storage costs, but it does not affect load performance. The load time is determined by file size and parallelism, not by post-load cleanup.
- ✗
Increase the warehouse size to a larger size.
Why it's wrong here
Increasing warehouse size may help, but if the file is a single large file, Snowflake cannot parallelize the load across multiple threads within that file. The bottleneck is often the single file, not compute resources. Splitting the file is more effective.
- ✗
Use a larger file format option to skip the header.
Why it's wrong here
Skipping the header is a minor optimization and does not address the core issue of loading a large single file. The file format option SKIP_HEADER=1 is used to ignore the header row, but it won't improve parallelism or speed significantly.
- ✓
Split the file into multiple smaller files and load them in parallel.
Why this is correct
Splitting a large file into multiple smaller files allows Snowflake to load them in parallel using multiple threads, significantly improving performance. This is a best practice for large data loads. The recommended size per file is 100-250 MB compressed.
About these practice questions
Courseiva writes every COF-C03 question from scratch — 280 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
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.