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NEW QUESTION 35
You are performing a semiannual capacity planning exercise for your flagship service. You expect a service user growth rate of 10% month-over-month over the next six months. Your service is fully containerized and runs on Google Cloud Platform (GCP). using a Google Kubernetes Engine (GKE) Standard regional cluster on three zones with cluster autoscaler enabled. You currently consume about 30% of your total deployed CPU capacity, and you require resilience against the failure of a zone. You want to ensure that your users experience minimal negative impact as a result of this growth or as a result of zone failure, while avoiding unnecessary costs. How should you prepare to handle the predicted growth?

  • A. Proactively add 60% more node capacity to account for six months of 10% growth rate, and then perform a load test to make sure you have enough capacity.
  • B. Because you are at only 30% utilization, you have significant headroom and you won't need to add any additional capacity for this rate of growth.
  • C. Because you are deployed on GKE and are using a cluster autoscaler. your GKE cluster will scale automatically, regardless of growth rate.
  • D. Verity the maximum node pool size, enable a horizontal pod autoscaler, and then perform a load test to verity your expected resource needs.

Answer: D

Explanation:
https://cloud.google.com/kubernetes-engine/docs/concepts/horizontalpodautoscaler The Horizontal Pod Autoscaler changes the shape of your Kubernetes workload by automatically increasing or decreasing the number of Pods in response to the workload's CPU or memory consumption

NEW QUESTION 36
You support an e-commerce application that runs on a large Google Kubernetes Engine (GKE) cluster deployed on-premises and on Google Cloud Platform. The application consists of microservices that run in containers. You want to identify containers that are using the most CPU and memory. What should you do?

  • A. Use Prometheus to collect and aggregate logs per container, and then analyze the results in Grafana.
  • B. Use Stackdriver Kubernetes Engine Monitoring.
  • C. Use the Stackdriver Monitoring API to create custom metrics, and then organize your containers using groups.
  • D. Use Stackdriver Logging to export application logs to BigOuery. aggregate logs per container, and then analyze CPU and memory consumption.

Answer: B

Explanation:
https://cloud.google.com/anthos/clusters/docs/on-prem/1.7/concepts/logging-and-monitoring

NEW QUESTION 37
You support a large service with a well-defined Service Level Objective (SLO). The development team deploys new releases of the service multiple times a week. If a major incident causes the service to miss its SLO, you want the development team to shift its focus from working on features to improving service reliability. What should you do before a major incident occurs?

  • A. Add a plugin to your Jenkins pipeline that prevents new releases whenever your service is out of SLO.
  • B. Develop an appropriate error budget policy in cooperation with all service stakeholders.
  • C. Negotiate with the product team to always prioritize service reliability over releasing new features.
  • D. Negotiate with the development team to reduce the release frequency to no more than once a week.

Answer: C

NEW QUESTION 38
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