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Google Cloud Certified Professional Cloud DevOps Engineer exam is designed to test the skills and knowledge of professionals who can help organizations improve their software delivery pipelines and build reliable, scalable, and secure applications on Google Cloud Platform (GCP). Google Cloud Certified - Professional Cloud DevOps Engineer Exam certification verifies that the candidate possesses the necessary skills to design, implement, and manage DevOps processes using GCP tools and services.
Google Professional-Cloud-DevOps-Engineer Certification is a comprehensive exam that evaluates the candidate's practical skills in the field of cloud computing and DevOps. Google Cloud Certified - Professional Cloud DevOps Engineer Exam certification is designed for professionals who have a good understanding of DevOps practices and tools, and can implement them in the GCP environment. Google Cloud Certified - Professional Cloud DevOps Engineer Exam certification is recognized globally and provides a competitive advantage to the candidates in the job market.
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The Google Professional-Cloud-DevOps-Engineer exam is designed to test the candidate's proficiency in using GCP services to deploy, manage and monitor applications, as well as their ability to implement best practices for application development, testing and deployment. Google Cloud Certified - Professional Cloud DevOps Engineer Exam certification exam requires a strong understanding of DevOps principles and tools, including containerization, automation, and infrastructure as code. Candidates must also have experience in using Google Cloud's tools and services, such as Cloud Storage, Cloud Functions, Cloud Pub/Sub, and Cloud Logging, to deploy and manage applications. The Professional-Cloud-DevOps-Engineer Certification can help IT professionals advance their careers and demonstrate their expertise in cloud computing and DevOps practices.
NEW QUESTION # 110
You use Spinnaker to deploy your application and have created a canary deployment stage in the pipeline. Your application has an in-memory cache that loads objects at start time. You want to automate the comparison of the canary version against the production version. How should you configure the canary analysis?
Answer: A
NEW QUESTION # 111
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?
Answer: A
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 # 112
You are troubleshooting a failed deployment in your CI/CD pipeline. The deployment logs indicate that the application container failed to start due to a missing environment variable. You need to identify the root cause and implement a solution within your CI/CD workflow to prevent this issue from recurring. What should you do?
Answer: B
NEW QUESTION # 113
You are developing the deployment and testing strategies for your CI/CD pipeline in Google Cloud You must be able to
* Reduce the complexity of release deployments and minimize the duration of deployment rollbacks
* Test real production traffic with a gradual increase in the number of affected users You want to select a deployment and testing strategy that meets your requirements What should you do?
Answer: B
Explanation:
The best option for selecting a deployment and testing strategy that meets your requirements is to use blue/green deployment and canary testing. A blue/green deployment is a deployment strategy that involves creating two identical environments, one running the current version of the application (blue) and one running the new version of the application (green). The traffic is switched from blue to green after testing the new version, and if any issues are discovered, the traffic can be switched back to blue instantly. This way, you can reduce the complexity of release deployments and minimize the duration of deployment rollbacks. A canary testing is a testing 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 real production traffic with a gradual increase in the number of affected users.
NEW QUESTION # 114
You are designing a deployment technique for your applications on Google Cloud. As part Of your deployment planning, you want to use live traffic to gather performance metrics for new versions Ofyour applications. You need to test against the full production load before your applications are launched. What should you do?
Answer: D
Explanation:
The correct answer is B. Use shadow testing with continuous deployment.
Shadow testing is a deployment technique that involves routing a copy of the live traffic to a new version of the application, without affecting the production environment. This way, you can gather performance metrics and compare them with the current version, without exposing the new version to the users. Shadow testing can help you test against the full production load and identify any issues or bottlenecks before launching the new version. You can use continuous deployment to automate the process of deploying the new version after it passes the shadow testing.
NEW QUESTION # 115
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