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Answer: Configure autoscaling for the Cloud Run service based on concurrency, and set up monitoring and alerting using Cloud Operations.
Configuring autoscaling for the Cloud Run service based on concurrency allows the system to dynamically allocate resources based on demand, improving performance during periods of high demand. Setting up monitoring and alerting using Cloud Operations enables you to proactively track the service‘s performance and react to issues. This approach aligns with SRE principles. - **Option A**: Incorrect. It may not address fluctuations in demand, potentially leading to performance bottlenecks or inefficient resource utilization. This approach doesn‘t adhere to SRE principles. - **Option B**: Incorrect. It may result in inefficient resource utilization and increased costs. This approach doesn‘t follow SRE principles, which emphasize making data-driven decisions and balancing performance with cost. - **Option C**: Incorrect. Disabling container instance logging and monitoring may reduce resource consumption, but it also prevents you from proactively tracking the service‘s performance and reacting to issues. This approach goes against SRE principles, which emphasize monitoring and alerting for maintaining service reliability and performance.
Author: LeetQuiz Editorial Team
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As a DevOps Engineer optimizing an API service on Google Cloud Platform's Cloud Run, which strategy ensures optimal performance during demand spikes while following Site Reliability Engineering (SRE) principles?
A
Migrate the service to a Compute Engine instance with the maximum available resources, without considering autoscaling.
B
Increase the allocated resources (CPU and memory) for each instance without analyzing the service‘s actual resource requirements.
C
Disable container instance logging and monitoring to reduce resource consumption.
D
Configure autoscaling for the Cloud Run service based on concurrency, and set up monitoring and alerting using Cloud Operations.
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