- A
Export the data to BigQuery periodically and run analytical queries there
BigQuery is designed for complex analytics; periodic exports ensure no impact on the OLTP instance.
- B
Enable MySQL Query Cache to speed up analytical queries on the primary instance
Why wrong: Query cache is limited and not suitable for large scans; it also impacts write performance.
- C
Use Cloud Spanner to handle both OLTP and analytics with interleaved tables
Why wrong: Spanner is not optimized for complex analytical queries; it is transactional.
- D
Create a Cloud SQL read replica and route analytical queries to it
Why wrong: Read replicas help with read offload, but complex analytical queries can still degrade replica performance.
Question 385 of 1,000
PCDOE Design and Plan Database Solutions Practice Question
This PCDOE practice question tests your understanding of design and plan database solutions. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A company is using Cloud SQL for MySQL for its OLTP workload. They want to run complex analytical queries on the same data without impacting transactional performance. The analytical queries involve large scans and joins. What is the recommended approach?
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
✓
Export the data to BigQuery periodically and run analytical queries there
Option A is correct because BigQuery is a serverless, highly scalable data warehouse designed for analytical queries on large datasets. By exporting data from Cloud SQL (OLTP) to BigQuery, you isolate the analytical workload from the transactional database, preventing resource contention and performance degradation on the primary instance. This separation of concerns is the recommended pattern for running complex scans and joins without impacting OLTP performance.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Export the data to BigQuery periodically and run analytical queries there
Why this is correct
BigQuery is designed for complex analytics; periodic exports ensure no impact on the OLTP instance.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Enable MySQL Query Cache to speed up analytical queries on the primary instance
Why it's wrong here
Query cache is limited and not suitable for large scans; it also impacts write performance.
- ✗
Use Cloud Spanner to handle both OLTP and analytics with interleaved tables
Why it's wrong here
Spanner is not optimized for complex analytical queries; it is transactional.
- ✗
Create a Cloud SQL read replica and route analytical queries to it
Why it's wrong here
Read replicas help with read offload, but complex analytical queries can still degrade replica performance.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google often tests the misconception that a read replica can handle analytical workloads without impact, but the trap here is that read replicas still run the same MySQL engine and share storage I/O, making them unsuitable for large scans and joins that would degrade performance for all queries on that replica.
Detailed technical explanation
How to think about this question
Under the hood, BigQuery uses a columnar storage format and a distributed execution engine (Dremel) that can process petabytes of data by dynamically allocating compute resources, whereas Cloud SQL's InnoDB engine uses row-based storage and a single-node architecture. A real-world scenario: a retail company running real-time order transactions on Cloud SQL can export daily sales data to BigQuery via a scheduled export or using federated queries, enabling complex cohort analysis and inventory forecasting without slowing down checkout operations.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
What to study next
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FAQ
Questions learners often ask
What does this PCDOE question test?
Design and Plan Database Solutions — This question tests Design and Plan Database Solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Export the data to BigQuery periodically and run analytical queries there — Option A is correct because BigQuery is a serverless, highly scalable data warehouse designed for analytical queries on large datasets. By exporting data from Cloud SQL (OLTP) to BigQuery, you isolate the analytical workload from the transactional database, preventing resource contention and performance degradation on the primary instance. This separation of concerns is the recommended pattern for running complex scans and joins without impacting OLTP performance.
What should I do if I get this PCDOE question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
This PCDOE 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 PCDOE exam.
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