COF-C03 Practice Question: Snowflake AI Data Cloud Features and Architecture
A data engineer is loading a 5 TB compressed CSV file into a Snowflake table using the COPY INTO command. The file is stored in an external stage pointing to an Amazon S3 bucket. The engineer notices that the load is slower than expected and wants to improve performance. Which of the following actions is most likely to improve the load performance?
⚠ Common exam trap
The trap here is assuming that increasing warehouse size will always speed up data loading, but COPY INTO parallelism is driven by the number of files, not warehouse size.
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 large CSV file into multiple smaller files and load them in parallel.
Snowflake's COPY INTO loads data in parallel by processing multiple files concurrently. When a single large file is loaded, it is handled by one thread, limiting throughput. Splitting the file into multiple smaller files enables Snowflake to use multiple threads and compute resources, thereby improving load performance. Other options do not directly address the parallelism limitation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use a larger file format that supports compression, such as Parquet.
Why it's wrong here
Changing the file format to Parquet may improve compression and query performance later, but it does not address the immediate bottleneck of loading a single large file. The load performance is constrained by file-level parallelism, not by the file format itself, unless the format allows splitting.
- ✗
Enable the VALIDATION_MODE parameter to speed up data validation.
Why it's wrong here
VALIDATION_MODE is used to validate data without loading it; it returns errors and does not actually load data. Enabling it would not speed up a real load and would instead prevent data from being loaded. It is not a performance tuning option for actual data ingestion.
- ✗
Increase the size of the virtual warehouse used for the load.
Why it's wrong here
While a larger warehouse provides more compute resources, the degree of parallelism for a COPY INTO operation is primarily determined by the number of files in the stage. A single large file will still be processed by one thread, so increasing warehouse size alone will not improve performance for a single-file load.
- ✓
Split the large CSV file into multiple smaller files and load them in parallel.
Why this is correct
Snowflake loads data in parallel by assigning each file to a separate thread. A single large file cannot be parallelized within itself, so splitting it into multiple smaller files (e.g., 100-250 MB compressed) allows Snowflake to distribute the load across multiple compute resources, significantly improving throughput.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
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.