Google Professional Cloud DevOps Engineer Exam Questions 2025
Google Professional Cloud DevOps Engineer Exam Questions 2025
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Question 1:
When constructing feedback loops to decide what to build next, which of the following is a key step to ensure the feedback is relevant and actionable?
A. Only soliciting feedback from internal stakeholders.
B. Gathering feedback from as many sources as possible.
C. Prioritizing feedback based on its impact on business goals.
D. Ignoring feedback that is critical of existing products or processes.
Answer: C
Explanation:
Prioritizing feedback based on its impact on business goals -> Correct. When constructing feedback loops, it is important to prioritize feedback based on its impact on business goals. This ensures that the feedback received is relevant and actionable, and that it will help drive the business forward. By prioritizing feedback, DevOps teams can focus on the most important areas for improvement and allocate resources accordingly.
Gathering feedback from as many sources as possible -> Incorrect. While gathering feedback from a wide range of sources can be helpful, it is not necessarily a key step to ensure that the feedback is relevant and actionable.
Only soliciting feedback from internal stakeholders -> Incorrect. Soliciting feedback only from internal stakeholders can result in a narrow focus that does not take into account the needs and perspectives of customers and end-users.
Ignoring feedback that is critical of existing products or processes -> Incorrect. Ignoring critical feedback can result in missed opportunities for improvement and hinder progress towards business goals.
Question 2:
You are a DevOps Engineer responsible for optimizing the performance of an API service running on Google Cloud Platform. The service is built using Cloud Run and experiences periodic spikes in demand. Which of the following strategies will best help you ensure optimal performance during periods of high demand while adhering to the principles of Site Reliability Engineering (SRE)?
A. Increase the allocated resources (CPU and memory) for each instance without analyzing the service‘s actual resource requirements.
B. Disable container instance logging and monitoring to reduce resource consumption.
C. Migrate the service to a Compute Engine instance with the maximum available resources, without considering autoscaling.
D. Configure autoscaling for the Cloud Run service based on concurrency, and set up monitoring and alerting using Cloud Operations.
Answer: D
Explanation:
Configure autoscaling for the Cloud Run service based on concurrency, and set up monitoring and alerting using Cloud Operations. -> Correct. Configuring autoscaling for the Cloud Run service based on concurrency allows the system to dynamically allocate resources based on demand, improving performance during periods of high demand. Setting up monitoring and alerting using Cloud Operations enables you to proactively track the service‘s performance and react to issues. This approach aligns with SRE principles.
Increase the allocated resources (CPU and memory) for each instance without analyzing the service‘s actual resource requirements. -> Incorrect. It may result in inefficient resource utilization and increased costs. This approach doesn‘t follow SRE principles, which emphasize making data-driven decisions and balancing performance with cost.
Migrate the service to a Compute Engine instance with the maximum available resources, without considering autoscaling. -> Incorrect. It may not address fluctuations in demand, potentially leading to performance bottlenecks or inefficient resource utilization. This approach doesn‘t adhere to SRE principles.
Disable container instance logging and monitoring to reduce resource consumption. -> Incorrect. Disabling container instance logging and monitoring may reduce resource consumption, but it also prevents you from proactively tracking the service‘s performance and reacting to issues. This approach goes against SRE principles, which emphasize monitoring and alerting for maintaining service reliability and performance.
Question 3:
As a Cloud DevOps Engineer, you are debugging an application running on Google Cloud. You decide to use Cloud Debugger to capture the state of your application at a specific point in the execution flow without stopping or slowing it down. What can you say about the data captured in a Cloud Debugger snapshot?
A. The snapshot includes the call stack and local variables at the snapshot location.
B. The snapshot contains the content of the local storage of the virtual machine running the application.
C. The snapshot includes only the stack trace of the current execution point.
D. The snapshot provides detailed logs of all operations performed by the application.
Answer: A
Explanation:
The snapshot includes the call stack and local variables at the snapshot location. -> Correct. A snapshot created by Cloud Debugger provides insight into the application‘s state without stopping its execution. The snapshot includes the call stack and the values of local variables at the specific snapshot location.
The snapshot contains the content of the local storage of the virtual machine running the application. -> Incorrect. The snapshot created by Cloud Debugger doesn‘t contain the content of the local storage of the virtual machine. It provides the application state at a specific point in time.
The snapshot includes only the stack trace of the current execution point. -. Incorrect. While the snapshot does include the stack trace, it also includes the values of local variables at the snapshot location. Therefore, it provides more context than just the stack trace.
The snapshot provides detailed logs of all operations performed by the application. -> Incorrect. Cloud Debugger snapshot doesn‘t provide detailed logs of all operations performed by the application. It focuses on capturing the state of the application at a particular point in time. For detailed logs, Cloud Logging would be the appropriate service.
Question 4:
As a DevOps Engineer, you have been tasked with optimizing the performance of a web application that frequently experiences sudden spikes in traffic. The application utilizes multiple microservices, and efficient resource utilization is a top priority. Which of the following strategies would be most effective in ensuring optimal performance and resource efficiency during traffic spikes?
A. Utilizing Google Kubernetes Engine (GKE) with cluster autoscaling enabled
B. Deploying the microservices on individual Compute Engine instances without autoscaling
C. Implementing a Cloud Load Balancer with global backend services
D. Implementing a custom cache eviction policy in Cloud CDN
Answer: A
Explanation:
Utilizing Google Kubernetes Engine (GKE) with cluster autoscaling enabled -> Correct. It allows the service to automatically scale based on demand, handling sudden spikes in user requests efficiently. GKE‘s cluster autoscaling feature can add or remove nodes in response to changing traffic patterns, ensuring that resources are efficiently allocated and application performance remains optimal.
Deploying the microservices on individual Compute Engine instances without autoscaling -> Incorrect. It does not provide an efficient way to manage resources and handle varying workloads. This approach might lead to underutilization of resources or performance issues during periods of high demand. Autoscaling and containerization, such as using Google Kubernetes Engine (GKE), would be more appropriate for optimizing performance and resource utilization in a microservices-based application.
Implementing a Cloud Load Balancer with global backend services -> Incorrect. While it can distribute traffic evenly among backend services, it does not inherently provide autoscaling capabilities. Autoscaling must be enabled separately, and using a load balancer alone would not be sufficient to handle sudden traffic spikes efficiently.
Implementing a custom cache eviction policy in Cloud CDN -> Incorrect. Cloud CDN does not provide support for custom cache eviction policies. Cache eviction policies in Cloud CDN are based on Cache-Control headers and the default time-to-live (TTL) settings. Additionally, Cloud CDN primarily focuses on caching static content, which is not the main concern for a microservices-based application experiencing sudden spikes in traffic.
Question 5:
You are building a CI/CD pipeline for a service that needs to be deployed to hybrid and multicloud environments. Which of the following is a best practice for building and deploying to hybrid and multicloud environments?
A. Use a different configuration file for each environment to manage environment-specific variables.
B. Use a different pipeline for each environment to ensure consistency and avoid conflicts.
C. Use a different container image for each environment to ensure consistency and avoid conflicts.
D. Use cloud-native tools and platforms to manage and deploy the service components.
Answer: D
Explanation:
Use cloud-native tools and platforms to manage and deploy the service components. -> Correct. Using cloud-native tools and platforms can help teams to manage and deploy service components efficiently and consistently across different hybrid and multicloud environments. It can also help to manage dependencies and version control, and ensure that the service components are deployed consistently across different environments.
Use a different pipeline for each environment to ensure consistency and avoid conflicts. -> Incorrect. It can make it harder to manage and track the deployment process across different environments. It can also lead to inconsistencies and make it harder to identify and resolve issues in specific environments.
Use a different configuration file for each environment to manage environment-specific variables. -> Incorrect. It can make it harder to manage and track configuration changes across different environments. It can also make it harder to identify and resolve issues in specific environments.
Use a different container image for each environment to ensure consistency and avoid conflicts. -> Incorrect. It can result in inconsistencies and make it harder to manage and track the deployment process across different environments. It can also lead to increased storage and management costs.
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Question 6:
When managing service incidents in Google Cloud Platform, a DevOps engineer needs to effectively coordinate roles and implement communication channels. During a major incident, your team needs to quickly identify the root cause and resolve the issue. Which of the following steps best demonstrates this process, while adhering to recommended practices in Google Cloud Platform?
A. Assign an Incident Commander and Communications Lead, create a dedicated Google Hangouts chat, update stakeholders using Statuspage, and perform a root cause analysis after the incident is resolved.
B. Assign a primary Incident Commander, create a dedicated Slack channel, share status updates through Google Sheets, and perform a root cause analysis after the incident is resolved.
C. Appoint an Incident Commander, use a shared company-wide Slack channel, skip status updates, and perform a root cause analysis only if stakeholders request it.
D. Designate a single team member to handle all roles, communicate via email, and perform a root cause analysis only if the incident recurs.
Answer: A
Explanation:
Assign an Incident Commander and Communications Lead, create a dedicated Google Hangouts chat, update stakeholders using Statuspage, and perform a root cause analysis after the incident is resolved. -> Correct. It follows Google Cloud‘s recommended practices for incident management. Assigning specific roles (Incident Commander and Communications Lead) ensures efficient coordination, while using a dedicated Google Hangouts chat enables real-time communication. Updating stakeholders using Statuspage keeps them informed, and performing a root cause analysis after the incident helps to prevent future occurrences.
Assign a primary Incident Commander, create a dedicated Slack channel, share status updates through Google Sheets, and perform a root cause analysis after the incident is resolved. -> Incorrect. This option is partially correct but falls short in terms of the communication tool used. Sharing status updates through Google Sheets is not an efficient way to communicate with stakeholders during an incident.
Designate a single team member to handle all roles, communicate via email, and perform a root cause analysis only if the incident recurs. -> Incorrect. Relying on a single team member to handle all roles is not a recommended practice as it may lead to bottlenecks and delays in resolving the issue. Email is not an ideal communication channel during incidents, and performing root cause analysis only if the incident recurs is not a good practice.
Appoint an Incident Commander, use a shared company-wide Slack channel, skip status updates, and perform a root cause analysis only if stakeholders request it. -> Incorrect. Using a shared company-wide Slack channel may cause confusion and is not the recommended practice. Skipping status updates and performing a root cause analysis only if stakeholders request it is not an efficient way to manage incidents.
Question 7:
As a Cloud DevOps Engineer, you are required to define Service Level Indicators (SLIs) for your cloud-based application. One of your key metrics is the successful processing of user requests. Which of the following options is the most appropriate approach for setting an SLI for this metric?
A. Define an SLI based on the amount of storage consumed by the application.
B. Define an SLI based on the number of users using the application at a given time.
C. Define an SLI based on the percentage of user requests successfully processed within a certain time frame.
D. Define an SLI based on the amount of compute resources used by the application.
Answer: C
Explanation:
Define an SLI based on the percentage of user requests successfully processed within a certain time frame. -> Correct. SLIs are often based on latency (how long it takes to return a response to a request), error rate (what fraction of requests fail), or availability (what fraction of the time the service is usable). So, an SLI based on the percentage of user requests successfully processed within a certain time frame is appropriate.
Define an SLI based on the amount of compute resources used by the application. -> Incorrect. While the amount of compute resources used by an application might be an important metric for capacity planning or cost optimization, it is not a Service Level Indicator (SLI). SLIs are specific quantitative measures of some aspect of the level of service that is provided.
Define an SLI based on the number of users using the application at a given time. -> Incorrect. The number of users using the application could be a useful business metric, but it is not an SLI. An SLI needs to reflect the level of service, such as the system‘s latency, error rate, or uptime.
Define an SLI based on the amount of storage consumed by the application. -> Incorrect. The amount of storage consumed by an application is a resource usage metric rather than an SLI.
Question 8:
You are a DevOps Engineer responsible for optimizing the performance of a service running on the Google App Engine local development server. The service is a critical part of your company‘s web application and must respond to user requests within 500 ms. You notice that the service is experiencing increased latency during peak hours. Which of the following approaches should you adopt to optimize the service‘s performance while using App Engine local development server in Google Cloud Platform?
A. Use manual scaling, increase the number of instances during peak hours, and reduce the number of instances during off-peak hours.
B. Implement a cache mechanism like Memcached or Redis to reduce the number of datastore reads.
C. Configure a cron job to restart the service every 15 minutes to ensure that the service runs efficiently.
D. Enable automatic scaling for the service, set a minimum and maximum number of instances, and configure appropriate latency targets.
Answer: B
Explanation:
Implement a cache mechanism like Memcached or Redis to reduce the number of datastore reads. -> Correct. It can help optimize service performance by reducing the number of datastore reads, resulting in lower latency during peak hours. This approach addresses the performance issue more directly and efficiently.
Enable automatic scaling for the service, set a minimum and maximum number of instances, and configure appropriate latency targets. -> Incorrect. Automatic scaling is not available for the App Engine local development server. It is only available for the App Engine standard environment and the App Engine flexible environment in the production environment.
Use manual scaling, increase the number of instances during peak hours, and reduce the number of instances during off-peak hours. -> Incorrect. Manual scaling could be a possible solution but requires constant monitoring and manual adjustments, making it inefficient and time-consuming.
Configure a cron job to restart the service every 15 minutes to ensure that the service runs efficiently. -> Incorrect. Restarting the service periodically may lead to unnecessary downtime and does not address the root cause of the performance issue.
Question 9:
In your role as a DevOps Engineer, you provide support for a web service that operates in multiple regions on Google Kubernetes Engine, utilizing a Global HTTP/S Cloud Load Balancer. Due to legacy considerations, user requests are initially directed through a third-party Content Delivery Network (CDN) before being routed to the Cloud Load Balancer. Currently, you have implemented a Service Level Indicator (SLI) for availability at the Cloud Load Balancer level. However, you aim to enhance coverage in the event of a potential misconfiguration of the load balancer, CDN failure, or any other major global networking disaster. Where should you measure this new SLI?
A. A synthetic client that periodically sends simulated user requests.
B. The health checks conducted by GKE for your application servers.
C. Client-side instrumentation implemented directly within the code.
D. The metrics that are extracted from the application servers.
E. The logs generated by your application servers.
Answer: A and C
Explanation:
As a Cloud DevOps Engineer, you are tasked with maintaining the health of instances within a Managed Instance Group (MIG). You have configured a health check for your MIG. How does the health check system determine that an instance in the MIG is unhealthy?
Question 10:
The instance is considered unhealthy if it doesn‘t return a 200 OK status within the check interval defined in the health check.
A. The instance is considered unhealthy if it hasn‘t been used for a specified period of time.
B. The instance is considered unhealthy if the CPU usage goes above 80%.
C. The instance is considered unhealthy if it does not respond to a ping.
Answer: A
Explanation:
The instance is considered unhealthy if it doesn‘t return a 200 OK status within the check interval defined in the health check. -> Correct. Health checks send HTTP, HTTPS, or TCP requests to each instance at a specified frequency. If an instance doesn‘t respond or doesn‘t return a 200 OK HTTP status to the health check system within the check interval defined, it is considered unhealthy.
The instance is considered unhealthy if it does not respond to a ping. -> Incorrect. While a lack of response to a ping could indicate an unhealthy instance, Google Cloud Health Checks do not use pings (ICMP Echo Request and Echo Reply messages) to determine the health status. They send HTTP, HTTPS, or TCP requests to the instance.
The instance is considered unhealthy if the CPU usage goes above 80%. -> Incorrect. High CPU usage does not automatically define an instance as unhealthy in a health check context. While it could indicate a potential issue, health checks are not designed to monitor CPU usage.
The instance is considered unhealthy if it hasn‘t been used for a specified period of time. -> Incorrect. Unused instances are not automatically considered unhealthy. Health checks are used to determine the responsiveness of the instance to a request, not usage metrics.
For a full set of 846 questions. Go to
https://skillcertpro.com/product/google-professional-cloud-devops-engineer-exam-questions/
SkillCertPro offers detailed explanations to each question which helps to understand the concepts better.
It is recommended to score above 85% in SkillCertPro exams before attempting a real exam.
SkillCertPro updates exam questions every 2 weeks.
You will get life time access and life time free updates
SkillCertPro assures 100% pass guarantee in first attempt.