Google PCA Design and plan a cloud solution architecture Practice Question
A financial services firm must design a data residency solution. Regulators require that customer personal data never leaves the country of origin, but the firm wants to use a single centralized analytics project for aggregated, non-personal reporting. Which Google Cloud architecture best satisfies both requirements?
⚠ Common exam trap
The trap here is believing that customer-managed encryption keys or column-level security satisfy data residency, when only the physical location of the stored data and enforced egress controls do.
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
✓
Store personal data in regional Cloud Storage buckets and BigQuery datasets located only in the required country, and use VPC Service Controls perimeters to prevent data egress, while aggregating anonymized metrics into the central analytics project.
Data residency is about where bytes are physically stored and replicated, so the architecture must pin personal data to regionally scoped datasets and buckets in the required country and use VPC Service Controls to stop egress. Encryption keys and network topology do not constrain physical location. Only anonymized aggregates may cross into the central analytics project.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Deploy a Shared VPC host project spanning multiple regions and use firewall rules to restrict which regions instances can reach, keeping all data logically within one network.
Why it's wrong here
A Shared VPC is a networking construct and does not control where storage or analytics data is physically stored or replicated. Firewall rules govern traffic, not data residency, and a multi-region network still allows data to be written to resources in other countries, so this approach cannot guarantee the personal data stays within the mandated country.
- ✗
Store all data in a multi-region Cloud Storage bucket and rely on customer-managed encryption keys to satisfy residency, aggregating data in the central project.
Why it's wrong here
A multi-region bucket may replicate data across multiple countries, which violates the requirement that personal data never leaves the country of origin. Customer-managed encryption keys control key ownership and rotation but do not constrain where data is physically stored or replicated, so this option fails the residency mandate regardless of key management.
- ✗
Use a global BigQuery dataset with column-level security and authorized views, then copy personal data into the central analytics project for processing.
Why it's wrong here
Column-level security and authorized views govern who can read fields, not where data resides, and BigQuery locations are set at dataset creation. Copying personal data into a central project would move it out of the required country, breaching the residency rule, so this design does not meet the regulatory constraint.
- ✓
Store personal data in regional Cloud Storage buckets and BigQuery datasets located only in the required country, and use VPC Service Controls perimeters to prevent data egress, while aggregating anonymized metrics into the central analytics project.
Why this is correct
This design keeps personal data in regionally scoped resources within the mandated country, uses VPC Service Controls to block egress of that data across perimeter boundaries, and only moves aggregated, anonymized metrics to the centralized project. It directly satisfies the residency rule while still enabling centralized reporting on non-personal data.
Go deeper
Related to this question
Learn chapter
Cloud SQL and Managed Data Stores
Key term
Project
A project is a temporary endeavor with a defined beginning and end, undertaken to create a unique product, service, or result, managed through specific processes in IT environments.
Key term
Data
Data is raw, unprocessed information, like numbers, words, or measurements, that can be stored, processed, and analyzed by computers.
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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.