Google Professional Cloud Database Engineer Question 88
Single answerGoogle Cloud PlatformYou are designing a database solution on Google Cloud for a retail company that requires high availability and scalability for their e-commerce platform. The company has a limited budget and wants to optimize costs while meeting their requirements. After assessing their workload, you determine that the database will have unpredictable traffic spikes during seasonal sales. Considering these factors, which database solution would be the most cost-effective?
- A
Cloud SQL with manual instance scaling
- B
Cloud Spanner with multi-region configuration
- C
Firestore in Datastore mode with on-demand capacity
- D
Cloud SQL with an autoscaling configuration
Show answer and explanation
Correct answer: C
Explanation
Firestore in Datastore mode with on-demand capacity is the most cost-effective solution for this scenario because it automatically scales with workload, charges based on actual usage, and eliminates the need for manual scaling or overprovisioning. This makes it ideal for handling unpredictable traffic spikes while optimizing costs. Other solutions like Cloud SQL or Cloud Spanner may either result in higher costs or require more management effort.
- A. Incorrect.
Cloud SQL with manual instance scaling may not be cost-effective for unpredictable traffic spikes, as you would need to overprovision resources to handle peak loads, leading to unnecessary costs during idle times.
- B. Incorrect.
Cloud Spanner with multi-region configuration is highly available and scalable, but it is one of the most expensive database solutions in Google Cloud, making it less suitable for a company with budget constraints.
- C. Correct.
Firestore in Datastore mode with on-demand capacity is a cost-effective option for unpredictable traffic patterns, as it automatically scales with workload and charges based on usage, avoiding overprovisioning costs.
- D. Incorrect.
Cloud SQL with an autoscaling configuration provides scalability, but it is still more expensive than Firestore for workloads requiring frequent scaling and handling unpredictable traffic spikes.