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Google Professional-Cloud-DevOps-Engineer Exam is a certification program designed to validate the skills and knowledge of professionals in the field of DevOps engineering on the Google Cloud Platform. Professional-Cloud-DevOps-Engineer exam is intended for individuals who are experienced in DevOps practices and have expertise in designing, building, and maintaining scalable and reliable cloud solutions.

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The Google Cloud Certified - Professional Cloud DevOps Engineer Exam certification exam tests the candidate's ability to design, implement, and manage a DevOps culture on the Google Cloud platform. Professional-Cloud-DevOps-Engineer Exam covers a wide range of topics, including infrastructure automation, configuration management, continuous integration and delivery, monitoring and logging, and incident management. Candidates are also expected to have hands-on experience with Google Cloud tools such as Kubernetes, Terraform, and Cloud Build.

Google Cloud Certified - Professional Cloud DevOps Engineer Exam Sample Questions (Q203-Q208):

NEW QUESTION # 203
You support a high-traffic web application with a microservice architecture. The home page of the application displays multiple widgets containing content such as the current weather, stock prices, and news headlines.
The main serving thread makes a call to a dedicated microservice for each widget and then lays out the homepage for the user. The microservices occasionally fail; when that happens, theserving thread serves the homepage with some missing content. Users of the application are unhappy if this degraded mode occurs too frequently, but they would rather have some content served instead of no content at all. You want to set a Service Level Objective (SLO) to ensure that the user experience does not degrade too much. What Service Level Indicator {SLI) should you use to measure this?

A. A freshness SLI: the proportion of widgets that have been updated within the last 10 minutes
B. A latency SLI: the ratio of microservice calls that complete in under 100 ms to the total number of microservice calls
C. An availability SLI: the ratio of healthy microservices to the total number of microservices
D. A quality SLI: the ratio of non-degraded responses to total responses

Answer: C

Explanation:
https://cloud.google.com/blog/products/gcp/available-or-not-that-is-the-question-cre-life-lessons

NEW QUESTION # 204
You have an application that runs on Cloud Run. You want to use live production traffic to test a new version of the application while you let the quality assurance team perform manual testing. You want to limit the potential impact of any issues while testing the new version, and you must be able to roll back to a previous version of the application if needed. How should you deploy the new version?
Choose 2 answers

A. Deploy a new Cloud Run revision with a tag and use the -no-traffic option.
B. Deploy the new application version and use the -no-traffic option Route production traffic to the revision's URL.
C. Deploy the new application version and split traffic to the new version.
D. Deploy the application as a new Cloud Run service.
E. Deploy a new Cloud Run revision without a tag and use the -no-traffic option.

Answer: A,C

NEW QUESTION # 205
Your team is designing a new application for deployment into Google Kubernetes Engine (GKE). You need to set up monitoring to collect and aggregate various application-level metrics in a centralized location. You want to use Google Cloud Platform services while minimizing the amount of work required to set up monitoring.
What should you do?

A. Install the OpenTelemetry client libraries in the application, configure Stackdriver as the export destination for the metrics, and then observe the application's metrics in Stackdriver.
B. Emit all metrics in the form of application-specific log messages, pass these messages from the containers to the Stackdriver logging collector, and then observe metrics in Stackdriver.
C. Install the Cloud Pub/Sub client libraries, push various metrics from the application to various topics, and then observe the aggregated metrics in Stackdriver.
D. Publish various metrics from the application directly to the Stackdriver Monitoring API, and then observe these custom metrics in Stackdriver.

Answer: A

NEW QUESTION # 206
You are the on-call Site Reliability Engineer for a microservice that is deployed to a Google Kubernetes Engine (GKE) Autopilot cluster. Your company runs an online store that publishes order messages to Pub/Sub and a microservice receives these messages and updates stock information in the warehousing system. A sales event caused an increase in orders, and the stock information is not being updated quickly enough. This is causing a large number of orders to be accepted for products that are out of stock You check the metrics for the microservice and compare them to typical levels.

You need to ensure that the warehouse system accurately reflects product inventory at the time orders are placed and minimize the impact on customers What should you do?

A. Decrease the acknowledgment deadline on the subscription
B. Increase the Pod CPU and memory limits
C. Increase the number of Pod replicas
D. Add a virtual queue to the online store that allows typical traffic levels

Answer: C

Explanation:
The best option for ensuring that the warehouse system accurately reflects product inventory at the time orders are placed and minimizing the impact on customers is to increase the number of Pod replicas.
Increasing the number of Pod replicas will increase the scalability and availability of your microservice, which will allow it to handle more Pub/Sub messages and update stock information faster. This way, you can reduce the backlog of undelivered messages and oldest unacknowledged message age, which are causing delays in updating product inventory. You can use Horizontal Pod Autoscaler or Cloud Monitoring metrics- based autoscaling to automatically adjust the number of Pod replicas based on load or custom metrics.

NEW QUESTION # 207
The new version of your containerized application has been tested and is ready to be deployed to production on Google Kubernetes Engine (GKE) You could not fully load-test the new version in your pre-production environment and you need to ensure that the application does not have performance problems after deployment Your deployment must be automated What should you do?

A. Deploy the application by using kubectl and use Config Connector to slowly ramp up traffic between versions. Use Cloud Monitoring to look for performance issues
B. Deploy the application through a continuous delivery pipeline by using canary deployments Use Cloud Monitoring to look for performance issues, and ramp up traffic as supported by the metrics
C. Deploy the application by using kubectl and set the spec. updatestrategy. type field to RollingUpdate Use Cloud Monitoring to look for performance issues, and run the kubectl rollback command if there are any issues.
D. Deploy the application through a continuous delivery pipeline by using blue/green deployments Migrate traffic to the new version of the application and use Cloud Monitoring to look for performance issues

Answer: B

Explanation:
The best option for deploying a new version of your containerized application to production on GKE and ensuring that the application does not have performance problems after deployment is to deploy the application through a continuous delivery pipeline by using canary deployments, use Cloud Monitoring to look for performance issues, and ramp up traffic as supported by the metrics. A canary deployment is a deployment strategy that involves releasing a new version of an application to a subset of users or servers and monitoring its performance and reliability. This way, you can test the new version in the production environment with real traffic and load, and gradually increase the traffic as the metrics indicate. You can use Cloud Monitoring to collect and analyze metrics from your application and GKE cluster, such as latency, error rate, CPU utilization, and memory usage. You can also use Cloud Monitoring to set up alerts and dashboards to track the performance of your application.

NEW QUESTION # 208
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