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Google Professional-Cloud-DevOps-Engineer is a prestigious certification designed for IT professionals who are interested in pursuing a career in cloud-based DevOps engineering. Google Cloud Certified - Professional Cloud DevOps Engineer Exam certification is intended for experienced IT professionals who have an in-depth understanding of DevOps practices and principles and who are skilled in implementing and managing cloud-based solutions. Google Professional-Cloud-DevOps-Engineer Certification is designed to validate the skills required to design, develop, and manage DevOps pipelines that leverage Google Cloud technologies.

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The Google Professional Cloud DevOps Engineer exam is taken by the specialists who want to obtain international certification and get validated as professional engineers who can manage Cloud DevOps features.

Google Cloud Certified - Professional Cloud DevOps Engineer Exam Sample Questions (Q11-Q16):

NEW QUESTION # 11
You work for a global organization and run a service with an availability target of 99% with limited engineering resources. For the current calendar month you noticed that the service has 99 5% availability. You must ensure that your service meets the defined availability goals and can react to business changes including the upcoming launch of new features You also need to reduce technical debt while minimizing operational costs You want to follow Google-recommended practices What should you do?

A. Add N+1 redundancy to your service by adding additional compute resources to the service
B. Identify, measure and eliminate toil by automating repetitive tasks
C. Define an error budget for your service level availability and minimize the remaining error budget
D. Allocate available engineers to the feature backlog while you ensure that the sen/ice remains within the availability target

Answer: C

NEW QUESTION # 12
Your company experiences bugs, outages, and slowness in its production systems. Developers use the production environment for new feature development and bug fixes. Configuration and experiments are done in the production environment, causing outages for users. Testers use the production environment for load testing, which often slows the production systems. You need to redesign the environment to reduce the number of bugs and outages in production and to enable testers to load test new features. What should you do?

A. Create a development environment with smaller server capacity and give access only to developers and testers.
B. Create a development environment for writing code and a test environment for configurations, experiments, and load testing.
C. Secure the production environment to ensure that developers can't change it and set up one controlled update per year.
D. Create an automated testing script in production to detect failures as soon as they occur.

Answer: B

Explanation:
Explanation
Creating a development environment for writing code and a test environment for configurations, experiments, and load testing is the best practice to reduce the number of bugs and outages in production and to enable testers to load test new features. This way, the production environment is isolated from changes that could affect its stability and performance.

NEW QUESTION # 13
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. Move the application from App Engine to Compute Engine.
B. Upgrade the GCS buckets to Multi-Regional.
C. Modify the App Engine configuration to have additional idle instances.
D. Enable high availability on the CloudSQL instances.

Answer: C

Explanation:
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 # 14
Your team has recently deployed an NGINX-based application into Google Kubernetes Engine (GKE) and has exposed it to the public via an HTTP Google Cloud Load Balancer (GCLB) ingress. You want to scale the deployment of the application's frontend using an appropriate Service Level Indicator (SLI). What should you do?

A. Configure the vertical pod autoscaler in GKE and enable the cluster autoscaler to scale the cluster as pods expand.
B. Expose the NGINX stats endpoint and configure the horizontal pod autoscaler to use the request metrics exposed by the NGINX deployment.
C. Configure the horizontal pod autoscaler to use the average response time from the Liveness and Readiness probes.
D. Install the Stackdriver custom metrics adapter and configure a horizontal pod autoscaler to use the number of requests provided by the GCLB.

Answer: D

Explanation:
https://cloud.google.com/kubernetes-engine/docs/tutorials/autoscaling-metrics

NEW QUESTION # 15
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. Bucketize the request latencies into ranges, and then compute the median and 90th percentiles.
B. 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.
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. Buckelize Ihe request latencies into ranges, and then compute the percentile at 100 ms.

Answer: D

NEW QUESTION # 16
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