Google Professional Cloud Network Engineer exam dumps

Google Professional Cloud Network Engineer practice question 401 of 790

Professional Cloud Network Engineer. Professional level, Google Cloud. Free question with the correct answer and a full explanation.

Google Professional Cloud Network Engineer Question 401

Single answerGoogle Cloud Platform

You are configuring a global HTTP(S) load balancer for a high-traffic web application hosted on Google Cloud. The application has backend services deployed in multiple regions, and each backend service uses instance groups with autoscaling enabled. You want to optimize traffic distribution to ensure that backend instances are not overloaded while maximizing performance for users. Which balancing method should you configure in the backend services to achieve this goal?

  1. A

    Rate-based balancing (RPS)

  2. B

    CPU utilization-based balancing

  3. C

    Utilization balancing based on memory usage

  4. D

    Round-robin balancing

Show answer and explanation

Correct answer: A

Explanation

Rate-based balancing (RPS) is the appropriate balancing method in this scenario because it ensures that individual backend instances are not overloaded by limiting the number of requests-per-second they handle. This method is particularly suitable for high-traffic web applications with autoscaling enabled, as it dynamically adjusts traffic distribution based on backend instance capacity.

  • A. Correct.

    Rate-based balancing (RPS) optimizes traffic distribution by limiting the number of requests-per-second sent to each backend instance. This is ideal for preventing overloading of backend instances while maintaining performance.

  • B. Incorrect.

    CPU utilization-based balancing is not supported directly by Google Cloud's global HTTP(S) load balancer, making it unsuitable for this use case.

  • C. Incorrect.

    Utilization balancing based on memory usage is not an available balancing method for backend services in Google Cloud.

  • D. Incorrect.

    Round-robin balancing distributes traffic evenly across backend instances but does not account for the load on individual instances, which could lead to overloading in high-traffic scenarios.

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