PDE Preparing and Using Data for Analysis Practice Question
You need to load a 2 TB CSV file from Cloud Storage into BigQuery. The CSV has a header row and uses a comma delimiter. You want to minimize cost and ensure the load completes quickly. Which method should you use?
⚠ Common exam trap
The trap here is choosing streaming or the web UI for a large file; batch loading from Cloud Storage is the recommended approach for bulk data.
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
✓
Run a `bq load` command with `--source_format=CSV` and `--skip_leading_rows=1`.
For large files in Cloud Storage, the most efficient and cost-effective method is a batch load using the `bq load` command or a load job. It supports CSV format, handles header rows with `--skip_leading_rows`, and parallelizes ingestion. This avoids the limitations of UI uploads and the overhead of streaming or scheduled transfers.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Run a `bq load` command with `--source_format=CSV` and `--skip_leading_rows=1`.
Why this is correct
The `bq load` command can load large files from Cloud Storage into BigQuery. Specifying `--source_format=CSV` and `--skip_leading_rows=1` handles the header row. This method is cost-effective (no data egress) and leverages BigQuery's parallel load capabilities for fast ingestion.
- ✗
Stream the data using the BigQuery Storage Write API.
Why it's wrong here
The Storage Write API is optimized for high-throughput streaming inserts, not for bulk loading from files. Streaming a 2 TB file would be slower and more expensive than a batch load, and it requires writing code to read and send data. It is not the right tool for this scenario.
- ✗
Use the BigQuery web UI to upload the file directly.
Why it's wrong here
The BigQuery web UI has a file size limit (typically 100 MB for direct uploads) and is not suitable for large files. Uploading a 2 TB file through the UI would fail or be extremely slow. For large files, you must use Cloud Storage as an intermediary.
- ✗
Use the BigQuery Data Transfer Service to schedule a recurring load.
Why it's wrong here
The BigQuery Data Transfer Service is designed for periodic data transfers from supported sources (e.g., Google Ads, Cloud Storage) but is not ideal for a one-time load of a single large file. It may introduce unnecessary scheduling overhead and does not provide the same immediate control as a direct load job.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
This PDE practice question is part of Courseiva's free Google Cloud 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 PDE exam.