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Google PCA Practice Question: An e-commerce company uses Cloud SQL for MySQL…
An e-commerce company uses Cloud SQL for MySQL for its transactional database. They need to run complex analytical queries on the same data without impacting OLTP performance. The analytical queries should be run on a read replica with minimal lag. Which solution is BEST?
⚠ Common exam trap
Candidates often mistakenly think exporting to BigQuery is the best solution for analytics on Cloud SQL data, but the trap is that the question explicitly requires 'minimal lag' and 'without impacting OLTP performance,' which a read replica achieves directly, whereas BigQuery introduces data movement delays and pipeline complexity.
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
✓
Create a Cloud SQL read replica and run analytical queries on it
Creating a Cloud SQL read replica allows you to offload analytical queries to a separate instance that replicates from the primary using MySQL's native asynchronous replication. This isolates the OLTP workload from heavy analytical queries, and with proper configuration (e.g., using a higher machine type and enabling InnoDB buffer pool tuning on the replica), you can achieve minimal replication lag. The read replica supports the same MySQL engine, so complex analytical queries run without schema changes or data movement.
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 Data Studio directly on Cloud SQL primary instance
Why it's wrong here
Data Studio queries execute against the primary instance, consuming the same CPU and I/O as OLTP transactions, so analytical load directly degrades transactional performance. It is tempting because Data Studio connects easily to Cloud SQL for visualisation, which suits small reporting needs where no separate replica exists.
- ✗
Migrate to Cloud Spanner to handle both workloads
Why it's wrong here
Cloud Spanner replaces the MySQL engine entirely, so it cannot serve as a read replica of Cloud SQL and offers no replication path with minimal lag from the existing primary. It is tempting because Spanner handles both transactional and analytical workloads at scale, which suits greenfield global applications rather than augmenting an existing Cloud SQL deployment.
- ✓
Create a Cloud SQL read replica and run analytical queries on it
Why this is correct
A Cloud SQL read replica receives asynchronous replication from the primary, so analytical queries execute on separate compute and storage, leaving the primary's OLTP capacity untouched. Replication lag is typically seconds, satisfying the minimal-lag requirement while isolating the analytical workload.
- ✗
Export the database to BigQuery and run queries there
Why it's wrong here
Exporting to BigQuery introduces ETL latency and does not provide a read replica of the live Cloud SQL MySQL instance, so analytical queries would reflect stale data rather than the minimal-lag requirement. This option is tempting because BigQuery excels at large-scale analytical workloads, and it would be correct if the requirement were to offload analytics to a separate, scalable data warehouse without needing near-real-time consistency with the transactional database.
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Related to this question
Learn chapter
Security Best Practices and Compliance
Key term
Data
Data is raw, unprocessed information, like numbers, words, or measurements, that can be stored, processed, and analyzed by computers.
Key term
OLTP
OLTP (Online Transaction Processing) is a data processing system designed to manage and record high volumes of real-time transactions, such as bank deposits or online purchases, quickly and reliably.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.