COF-C03 Data Loading, Unloading, and Connectivity Practice Question
A data engineer needs to load a 4.2 GB uncompressed CSV file from an internal stage into a Snowflake table. The file cannot be split because the CSV has embedded newlines within quoted fields. The engineer wants to maximize load performance. What should the engineer do?
⚠ Common exam trap
The trap here is assuming that a larger virtual warehouse or compression alone will parallelize a single large file, when Snowflake parallelizes only across separate files.
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 (e.g., 100-250 MB compressed) and stage them together, then run a single COPY INTO command referencing the stage path.
Snowflake achieves load parallelism by processing multiple files concurrently, not by splitting a single file. When a file cannot be split due to embedded newlines, the engineer must manually divide it into multiple smaller files and stage them together. A single COPY INTO command can then load all files in parallel, significantly improving performance over loading one large file.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Load the file using the Snowpipe REST API with the insertFiles endpoint, which automatically splits large files into chunks for parallel ingestion.
Why it's wrong here
The Snowpipe REST API does not split files into chunks; it queues files for ingestion and relies on the same file-level parallelism as COPY INTO. A single large file remains a single unit of work. The insertFiles endpoint only registers files for loading and does not perform any splitting or chunking of file contents.
- ✗
Use a larger virtual warehouse and load the single file; Snowflake will automatically parallelize the load across all compute resources in the warehouse.
Why it's wrong here
Snowflake parallelizes loads by distributing individual files across threads, not by splitting a single file. A single large file, even on a larger warehouse, is processed by one thread. Increasing warehouse size adds compute resources but does not help when only one file is available, so throughput remains constrained by single-thread processing.
- ✓
Split the file into multiple smaller files (e.g., 100-250 MB compressed) and stage them together, then run a single COPY INTO command referencing the stage path.
Why this is correct
Splitting into multiple files allows Snowflake to distribute them across threads in the warehouse, enabling parallel loading. The recommended compressed size is 100-250 MB per file. A single COPY INTO command can reference the stage path and will load all files in parallel, maximizing throughput while respecting the CSV's embedded newline constraint.
- ✗
Compress the file with gzip and load it as a single file; Snowflake will automatically split the compressed file across all nodes in the warehouse.
Why it's wrong here
Compressing with gzip reduces storage and network transfer, but Snowflake cannot split a gzip file for parallel processing because gzip is not splittable. A single 4.2 GB gzip file will be processed by only one thread, severely limiting load throughput. To parallelize, the engineer must split the file into multiple smaller files before compressing.
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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.