PDE Storing the Data Practice Question
A logistics company runs Cloud SQL for MySQL for its order management system. The database is 2 TB and growing 100 GB per month. They need to run analytical queries without impacting transactional performance, and they want to minimize operational overhead. They also require the analytics data to be no more than 15 minutes stale. Which storage approach should they use?
⚠ Common exam trap
The trap here is assuming a read replica is sufficient for analytics, when heavy scans can still cause replication lag and resource contention.
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
✓
Configure a Datastream stream from Cloud SQL to BigQuery with a change data capture policy and query the replicated tables in BigQuery.
Datastream provides serverless change data capture from Cloud SQL for MySQL into BigQuery, keeping the analytics copy within minutes of the source. BigQuery isolates analytical scans from the transactional instance, so order management performance is unaffected. The managed service minimizes operational overhead compared to maintaining replicas or custom export jobs, and it satisfies the 15-minute freshness requirement through continuous replication.
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 Cloud SQL database to a Cloud Storage bucket nightly with mysqldump, then load the files into BigQuery each morning.
Why it's wrong here
A nightly export creates a batch pipeline with up to 24 hours of staleness, far exceeding the 15-minute requirement. It also requires scripting and monitoring of export and load jobs, adding operational overhead. The full-dump approach is inefficient for a 2 TB database growing 100 GB monthly, and it does not provide continuous change data capture.
- ✗
Enable the Cloud SQL federated query feature in BigQuery and query the Cloud SQL instance directly from BigQuery.
Why it's wrong here
BigQuery federated queries to Cloud SQL use the Cloud SQL connection and execute queries against the operational database, which can impact transactional performance. They are intended for small lookups and joins, not for scanning a 2 TB analytical dataset. This approach would compete with order management workloads and does not provide the isolation or scalability required.
- ✗
Create a read replica of the Cloud SQL instance and run analytical queries directly against the replica.
Why it's wrong here
A read replica offloads read traffic from the primary, but it scales vertically and is not designed for large analytical scans. Running heavy aggregations on a replica can still cause replication lag and resource contention. It also requires managing another instance, and the 2 TB size with 100 GB monthly growth makes this approach costly and operationally heavier than a purpose-built analytical store.
- ✓
Configure a Datastream stream from Cloud SQL to BigQuery with a change data capture policy and query the replicated tables in BigQuery.
Why this is correct
Datastream replicates changes from Cloud SQL for MySQL to BigQuery using change data capture, keeping the analytics copy fresh within minutes. BigQuery handles large analytical scans without affecting the transactional database and requires minimal operational management. This satisfies the 15-minute staleness requirement and the need to avoid impacting order management performance, while minimizing overhead compared to managing replicas.
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 |
Go deeper
Related to this question
About these practice questions
This PDE question is part of Courseiva's 747-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
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 PDE 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 PDE exam.