Google Associate Cloud Engineer exam dumps

Google Associate Cloud Engineer practice question 225 of 375

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

Google Associate Cloud Engineer Question 225

Select 3Google Cloud Platform

As a Google Cloud Associate Cloud Engineer, you have been tasked with configuring an autoscaling policy for a managed instance group that handles web traffic on Compute Engine. The client expects variable traffic patterns with peaks during specific hours of the day. Which of the following actions should you take to efficiently configure autoscaling parameters?

  1. A

    Set a minimum number of instances to handle the predictable baseline traffic.

  2. B

    Configure a cooldown period to ensure stable scaling actions.

  3. C

    Use average CPU utilization as the sole metric for scaling decisions.

  4. D

    Enable predictive autoscaling to anticipate traffic spikes.

Show answer and explanation

Correct answers: A, B, D

Explanation

To effectively manage autoscaling for a web application with variable traffic, setting a minimum number of instances ensures baseline coverage, while configuring cooldown periods helps maintain stability during scaling actions. Enabling predictive autoscaling allows for better anticipation of traffic spikes, enhancing overall responsiveness. Relying only on CPU utilization may lead to suboptimal scaling decisions if other factors are ignored.

  • A. Correct.

    Setting a minimum number of instances ensures that there is always enough capacity to handle baseline traffic, preventing service disruptions.

  • B. Correct.

    Configuring a cooldown period helps prevent rapid scaling actions, allowing the system to stabilize and avoid unnecessary cost.

  • C. Incorrect.

    Relying solely on average CPU utilization can be limiting as it may not account for other important metrics like memory usage or request rate.

  • D. Correct.

    Predictive autoscaling uses machine learning to anticipate traffic patterns, offering a proactive approach to scaling.

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