ARA-C01 Data Engineering Practice Question
A retail company ingests a daily 2 TB CSV file into a Snowflake table. The file is stored in an internal stage. The COPY INTO command currently runs with a single large file, and the load takes over 4 hours. The architect wants to reduce load time by leveraging parallelism. Which action should the architect take?
⚠ Common exam trap
The trap here is assuming that a larger warehouse will automatically speed up a single-file load, but parallelism requires multiple 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 large CSV file into multiple smaller files (e.g., 100–250 MB each) and use a single COPY INTO command with a pattern to load all files.
The key to faster loads is parallelizing across multiple files. A single large file is loaded by one thread, so splitting into smaller files allows Snowflake to distribute the load across many threads, significantly reducing elapsed time. Increasing warehouse size or changing format does not address the single-file bottleneck.
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 the VALIDATION_MODE = RETURN_ERRORS option to check for errors before loading, then rerun the COPY INTO command.
Why it's wrong here
VALIDATION_MODE only validates the file and returns errors or a sample of rows; it does not load data or improve load performance. It is useful for debugging but does not address parallelism or speed of the actual load.
- ✓
Split the large CSV file into multiple smaller files (e.g., 100–250 MB each) and use a single COPY INTO command with a pattern to load all files.
Why this is correct
Splitting the file enables Snowflake to load multiple files in parallel across the compute resources of the warehouse. A single COPY INTO with a pattern can reference all files, and the load is distributed. This is the recommended approach for large data loads.
- ✗
Increase the warehouse size to 4X-Large and rerun the same COPY INTO command on the single large file.
Why it's wrong here
While a larger warehouse provides more compute, a single large file cannot be parallelized within COPY INTO. Snowflake loads each file using one thread; thus, increasing warehouse size does not help when only one file is present. The bottleneck is the single file, not compute.
- ✗
Convert the CSV file to JSON and load it using the VARIANT data type with a single COPY INTO command.
Why it's wrong here
Changing the file format to JSON does not inherently improve load performance. The load still processes one file, and JSON parsing may add overhead. The issue is lack of parallelism due to a single file, not the file format.
About these practice questions
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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 ARA-C01 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 ARA-C01 exam.