Cloud Digital Leader Practice Question: Google Cloud products, services, and solutions
A company uses Cloud SQL for PostgreSQL and needs to run complex analytical queries on the same dataset without affecting the performance of the transactional database. What should they 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
✓
Create read replicas of the Cloud SQL instance and run queries on the replicas
Cloud SQL read replicas maintain a continuously updated copy of the primary instance and serve read-only traffic, so complex analytical queries can be offloaded to the replica without consuming the primary instance's CPU, memory, or I/O resources. This isolates the transactional workload from the analytical workload while keeping the same PostgreSQL dataset. Option A is not ideal because periodic exports are stale and require managing a separate BigQuery pipeline, not real-time querying of the same dataset. Option C only adds capacity to the primary instance and does not separate analytical load from transactional traffic. Option D is unsuitable because BigQuery federated queries against Cloud SQL still send query load to the Cloud SQL instance, affecting transactional performance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Schedule periodic exports to Cloud Storage and query with BigQuery
Why it's wrong here
Periodic exports are batch snapshots, so queries run against stale data rather than the live dataset. Exporting to Cloud Storage and querying with BigQuery is correct when freshness is unimportant and cost-effective historical analysis of large volumes is the goal.
- ✓
Create read replicas of the Cloud SQL instance and run queries on the replicas
Why this is correct
Read replicas receive asynchronous copies of the primary's data, letting analytical queries run on separate compute without consuming the transactional instance's resources. This isolates workload contention, directly satisfying the requirement to avoid performance impact on the production database.
- ✗
Upgrade the Cloud SQL instance to a higher machine type
Why it's wrong here
A larger machine type gives the instance more CPU and memory, but analytical queries still run on the same instance and contend with transactional traffic. Vertical scaling is the right answer when the workload itself is uniformly resource-constrained, not when it must be separated.
- ✗
Use BigQuery to directly query Cloud SQL via federated queries
Why it's wrong here
Federated queries execute against the Cloud SQL instance itself, consuming its CPU and connections, so transactional performance still degrades. BigQuery federated queries suit ad-hoc exploration of external data, not isolating heavy analytical workloads from a production database.
Go deeper
Related to this question
Learn chapter
Choosing the Right Database on GCP
Key term
Cloud SQL
Cloud SQL is a fully managed relational database service that lets you set up, maintain, and scale SQL databases (like MySQL, PostgreSQL, and SQL Server) in the cloud without managing the underlying infrastructure.
Key term
SQL
SQL is a standardized programming language used to manage and manipulate relational databases, enabling querying, updating, and data retrieval.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This GCDL 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 GCDL exam.