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NEW QUESTION 22
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. Install an Application Performance Monitoring (APM) tool in both locations, and configure an export to a central data storage location for analysis.B. Import the Stackdriver Profiler package, and configure it to relay function timing data to Stackdriver for further analysis.C. Instrument the code using a timing library, and publish the metrics via a health check endpoint that is scraped by Stackdriver.D. Import the Stackdriver Debugger package, and configure the application to emit debug messages with timing information.

Answer: B

 

NEW QUESTION 23
You support a web application that runs on App Engine and uses CloudSQL and Cloud Storage for data storage. After a short spike in website traffic, you notice a big increase in latency for all user requests, increase in CPU use, and the number of processes running the application. Initial troubleshooting reveals:
After the initial spike in traffic, load levels returned to normal but users still experience high latency.
Requests for content from the CloudSQL database and images from Cloud Storage show the same high latency.
No changes were made to the website around the time the latency increased.
There is no increase in the number of errors to the users.
You expect another spike in website traffic in the coming days and want to make sure users don't experience latency. What should you do?

A. Upgrade the GCS buckets to Multi-Regional.B. Modify the App Engine configuration to have additional idle instances.C. Enable high availability on the CloudSQL instances.D. Move the application from App Engine to Compute Engine.

Answer: B

Explanation:
Scaling App Engine scales the number of instances automatically in response to processing volume. This scaling factors in the automatic_scaling settings that are provided on a per-version basis in the configuration file. A service with basic scaling is configured by setting the maximum number of instances in the max_instances parameter of the basic_scaling setting. The number of live instances scales with the processing volume. You configure the number of instances of each version in that service's configuration file. The number of instances usually corresponds to the size of a dataset being held in memory or the desired throughput for offline work. You can adjust the number of instances of a manually-scaled version very quickly, without stopping instances that are currently running, using the Modules API set_num_instances function. https://cloud.google.com/appengine/docs/standard/python/how-instances-are-managed
https://cloud.google.com/appengine/docs/standard/python/config/appref
max_idle_instances Optional. The maximum number of idle instances that App Engine should maintain for this version. Specify a value from 1 to 1000. If not specified, the default value is automatic, which means App Engine will manage the number of idle instances. Keep the following in mind: A high maximum reduces the number of idle instances more gradually when load levels return to normal after a spike. This helps your application maintain steady performance through fluctuations in request load, but also raises the number of idle instances (and consequent running costs) during such periods of heavy load.

 

NEW QUESTION 24
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. Verity the maximum node pool size, enable a horizontal pod autoscaler, and then perform a load test to verity your expected resource needs.D. Because you are deployed on GKE and are using a cluster autoscaler. your GKE cluster will scale automatically, regardless of growth rate.

Answer: C

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 25
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