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Exam Professional Cloud Developer topic 1 question 305 discussion

Actual exam question from Google's Professional Cloud Developer
Question #: 305
Topic #: 1
[All Professional Cloud Developer Questions]

Your ecommerce application has a rapidly growing user base, and it is experiencing performance issues due to excessive requests to your backend API. Your team develops and manages this API. The Cloud SQL backend database is struggling to handle the high demand, leading to latency and timeouts. You need to implement a solution that optimizes API performance and improves user experience. What should you do?

  • A. Use Apigee to expose your API. Use Memorystore for Redis to cache frequently accessed data. Implement exponential backoff in the application to retry failed requests.
  • B. Use Apigee to expose your API. Implement rate limiting and access control policies in Apigee to control API traffic. Use Pub/Sub to queue requests to prevent database overload.
  • C. Use Cloud Load Balancing to expose your API. Use Cloud CDN in front of the load balancer to cache responses. Implement exponential backoff to retry failed requests.
  • D. Use Cloud Load Balancing to expose your API. Increase the memory for the database instances to handle more concurrent requests. Implement a custom rate-limiting mechanism in your application code to control API requests.
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Suggested Answer: A 🗳️
by anshad666 at Oct. 12, 2024, 8:39 a.m.

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anshad666
1 month, 1 week ago
Selected Answer: A
Apigee: Using Apigee to expose your API helps manage, secure, and scale your API. Apigee provides robust traffic management features like rate limiting, analytics, and security, making it ideal for handling a growing user base. Memorystore for Redis: Caching frequently accessed data in Memorystore (a managed Redis service) offloads repetitive read requests from your Cloud SQL backend, reducing database load and improving response times. Exponential Backoff: Implementing exponential backoff in the application ensures smoother handling of failed or timed-out requests by retrying them with increasing delays, preventing system overload during high traffic periods.
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