Google Professional Cloud Developer Question 37
Select 3Google Cloud PlatformYou are developing an e-commerce application on Google Cloud that experiences high traffic during sales events. To reduce latency and offload backend systems, you decide to implement a caching solution. Which options represent appropriate caching strategies for this scenario?
- A
Use Memorystore for Redis to store frequently accessed product data such as pricing and inventory details.
- B
Cache API responses using Google Cloud CDN for static and dynamic content.
- C
Store cached data in Google Cloud Storage buckets for low-latency access to frequently accessed data.
- D
Leverage Memorystore for Memcached to cache user session data and reduce database query load.
- E
Use Google BigQuery to store and query frequently accessed product data for better performance.
Show answer and explanation
Correct answers: A, B, D
Explanation
To handle high traffic during events, a combination of low-latency in-memory caching (e.g., Memorystore for Redis or Memcached) and edge caching (e.g., Google Cloud CDN) provides optimal performance improvements by reducing backend load and latency. Google Cloud Storage and BigQuery are not intended for real-time caching scenarios.
- A. Correct.
Correct: Memorystore for Redis is designed for low-latency in-memory caching, making it suitable for storing frequently accessed product data like pricing and inventory details.
- B. Correct.
Correct: Google Cloud CDN can cache both static and dynamic API responses at distributed edge locations, reducing latency and backend load for high-traffic applications.
- C. Incorrect.
Incorrect: Google Cloud Storage is designed for large-scale object storage and is not optimized for low-latency caching use cases.
- D. Correct.
Correct: Memorystore for Memcached is ideal for caching transient data such as user sessions, helping reduce database query load during high traffic.
- E. Incorrect.
Incorrect: Google BigQuery is a data warehousing solution designed for analytics rather than low-latency caching.