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.Verity the maximum node pool size, enable a horizontal pod autoscaler, and then perform a load test to verity your expected resource needs.
• B.Because you are deployed on GKE and are using a cluster autoscaler. your GKE cluster will scale automatically, regardless of growth rate.
• C.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.
• D.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.

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You have a set of applications running on a Google Kubernetes Engine (GKE) cluster, and you are using Stackdriver Kubernetes Engine Monitoring. You are bringing a new containerized application required by your company into production. This application is written by a third party and cannot be modified or reconfigured. The application writes its log information to /var/log/app_messages.log, and you want to send these log entries to Stackdriver Logging. What should you do?

• A.Use the default Stackdriver Kubernetes Engine Monitoring agent configuration.
• B.Deploy a Fluentd daemonset to GKE. Then create a customized input and output configuration to tail the log file in the application's pods and write to Slackdriver Logging.
• C.Install Kubernetes on Google Compute Engine (GCE> and redeploy your applications. Then customize the built-in Stackdriver Logging configuration to tail the log file in the application's pods and write to Stackdriver Logging.
• D.Write a script to tail the log file within the pod and write entries to standard output. Run the script as a sidecar container with the application's pod. Configure a shared volume between the containers to allow the script to have read access to /var/log in the application container.

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You support a high-traffic web application and want to ensure that the home page loads in a timely manner. As a first step, you decide to implement a Service Level Indicator (SLI) to represent home page request latency with an acceptable page load time set to 100 ms. What is the Google-recommended way of calculating this SLI?

• A.Count the number of home page requests that load in under 100 ms. and then divide by the total number of all web application requests.
• B.Buckelize Ihe request latencies into ranges, and then compute the percentile at 100 ms.
• C.Count the number of home page requests that load in under 100 ms, and then divide by the total number of home page requests.
• D.Bucketize the request latencies into ranges, and then compute the median and 90th percentiles.

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Your team is designing a new application for deployment both inside and outside Google Cloud Platform (GCP). You need to collect detailed metrics such as system resource utilization. You want to use centralized GCP services while minimizing the amount of work required to set up this collection system. What should you do?

• A.Import the Stackdriver Profiler package, and configure it to relay function timing data to Stackdriver for further analysis.
• B.Import the Stackdriver Debugger package, and configure the application to emit debug messages with timing information.
• C.Instrument the code using a timing library, and publish the metrics via a health check endpoint that is scraped by Stackdriver.
• D.Install an Application Performance Monitoring (APM) tool in both locations, and configure an export to a central data storage location for analysis.

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Your organization recently adopted a container-based workflow for application development. Your team develops numerous applications that are deployed continuously through an automated build pipeline to the production environment. A recent security audit alerted your team that the code pushed to production could contain vulnerabilities and that the existing tooling around virtual machine (VM) vulnerabilities no longer applies to the containerized environment. You need to ensure the security and patch level of all code running through the pipeline. What should you do?

• A.Set up Container Analysis to scan and report Common Vulnerabilities and Exposures.
• B.Configure the containers in the build pipeline to always update themselves before release.
• C.Reconfigure the existing operating system vulnerability software to exist inside the container.
• D.Implement static code analysis tooling against the Docker files used to create the containers.

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