Google PCA Manage and provision cloud infrastructure Practice Question
A company is migrating its on-premises data warehouse to BigQuery. The data is currently stored in several CSV files on a Compute Engine instance. The company needs to load the data into BigQuery once and then perform complex analytical queries. The data volume is about 10 TB, and the company wants to minimize cost and loading time. Which approach should the architect recommend?
⚠ Common exam trap
The trap here is assuming that a direct load from Compute Engine or a custom Dataproc job is needed, when the native Cloud Storage to BigQuery load job is simpler, faster, and cheaper.
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
✓
Upload the CSV files to a Cloud Storage bucket, then use a BigQuery load job to load the data from Cloud Storage into a BigQuery table.
A BigQuery load job from Cloud Storage is the most efficient and cost-effective method for a one-time load of 10 TB. It leverages massively parallel loading, has no loading charges, and avoids the overhead of managing additional services. Uploading to Cloud Storage first is a standard best practice for large-scale data ingestion into BigQuery.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Create a Dataproc cluster and run a Spark job to read the CSV files and write them to BigQuery.
Why it's wrong here
Using Dataproc with Spark adds unnecessary complexity and cost for a simple one-time load. While it can handle large datasets, it requires managing a cluster and writing code, which is not cost-effective compared to a native BigQuery load job. It does not minimize cost or loading time for this straightforward migration.
- ✗
Use BigQuery Data Transfer Service to schedule a recurring transfer from the Compute Engine instance.
Why it's wrong here
BigQuery Data Transfer Service supports transfers from specific sources like Cloud Storage, Amazon S3, and SaaS applications, but not directly from a Compute Engine instance's local file system. It is designed for recurring transfers, not a one-time load. This approach is not applicable to the scenario and would not work without first moving the data to a supported source.
- ✓
Upload the CSV files to a Cloud Storage bucket, then use a BigQuery load job to load the data from Cloud Storage into a BigQuery table.
Why this is correct
Uploading the CSV files to Cloud Storage and then using a BigQuery load job is the recommended approach. BigQuery load jobs from Cloud Storage are fast, support parallel loading, and are free for loading data (you only pay for storage and queries). This minimizes loading time and cost for a 10 TB dataset.
- ✗
Use the bq command-line tool to load the CSV files directly from the Compute Engine instance into BigQuery.
Why it's wrong here
Loading CSV files directly from a Compute Engine instance using the bq tool is possible, but it is slower and less reliable for 10 TB of data due to network transfer and the lack of parallelization. It also incurs egress charges if the instance is in a different region, and it does not leverage the distributed loading capabilities of Google Cloud.
Go deeper
Related to this question
Learn chapter
Virtual Machine Instances in Compute Engine
Key term
BigQuery
BigQuery is a fully managed, serverless data warehouse on Google Cloud that lets you run fast SQL queries on massive datasets without managing any infrastructure.
Key term
Data warehouse
A data warehouse is a central repository that stores large amounts of structured data from multiple sources, optimized for querying and analysis rather than day-to-day transactions.
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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 Google Cloud exam blueprint
This PCA 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 PCA exam.