Google Professional Cloud DevOps Engineer Exam Dumps & Practice Test Questions

Question 1:

You are managing a production-level Node.js application deployed on Google Kubernetes Engine (GKE). This application relies heavily on making HTTP calls to several external and internal services. To prevent potential slowdowns in your application, you want to identify which of these dependencies might be contributing to performance issues. 

What is the best method to proactively analyze and detect bottlenecks in communication between these services?

A. Use Stackdriver Profiler to monitor all applications.
B. Implement Stackdriver Trace across all services and examine HTTP interactions.
C. Leverage Stackdriver Debugger to analyze logic execution across applications.
D. Update the Node.js code to log HTTP timings, then analyze with Stackdriver Logging.

Correct Answer: B

Explanation:

In distributed and microservices-based architectures—such as applications running on GKE—it is essential to understand the flow of inter-service communication to detect and resolve performance bottlenecks early. Often, the performance of one microservice can negatively affect others, especially when they are tightly coupled through HTTP requests.

Option A refers to Stackdriver Profiler (now Cloud Profiler), which helps developers identify areas in the code consuming excessive CPU or memory resources. While this tool is valuable for improving individual service efficiency, it doesn't offer visibility into HTTP request chains or latency between services, which are key to identifying problematic dependencies.

Option B, the correct answer, recommends implementing Stackdriver Trace (now Cloud Trace) to instrument all services. Cloud Trace is designed for distributed tracing, which maps out how requests flow through various services. By using it, you can visualize each HTTP request’s journey, assess latency at each hop, and spot any service that may be introducing slowness. It allows you to see bottlenecks clearly and identify whether an internal or third-party service is affecting the end-user experience. This proactive insight into inter-service latency makes Cloud Trace the best tool for identifying slow-performing dependencies.

Option C discusses using Stackdriver Debugger, which helps inspect application state during runtime. Although helpful for diagnosing logic errors or inspecting variables, it is not suitable for observing end-to-end performance across services.

Option D involves manually adding logging for request durations and analyzing them through Stackdriver Logging (now Cloud Logging). While this approach can surface some insights, it lacks the structured, end-to-end view offered by Cloud Trace. Manual logging is also harder to scale and maintain, especially in dynamic environments with many services.

In summary, distributed tracing via Cloud Trace enables you to track performance across services seamlessly and effectively. It visualizes latency and request paths, enabling operations teams to act before users are impacted. This makes Option B the most appropriate and proactive solution for identifying performance issues among dependent applications.

Question 2:

You have created a monitoring chart that displays CPU usage in Google Cloud's operations suite and added it to a dashboard within your project workspace. You need to share this chart with your Site Reliability Engineering (SRE) team, but want to follow the principle of least privilege—granting only the permissions required to view the chart and nothing more. 

What is the most secure way to grant them access?

A. Provide the project ID and grant the team the Monitoring Viewer role.
B. Share the project ID and grant the team the Dashboard Viewer role.
C. Use the “Share chart by URL” feature and assign the Monitoring Viewer role.
D. Use the “Share chart by URL” feature and assign the Dashboard Viewer role.

Correct Answer: D

Explanation:

When managing access in Google Cloud, it is crucial to follow the principle of least privilege—providing users only the permissions they need to perform their specific tasks, no more and no less. In this case, your goal is to let your SRE team view a specific chart, without giving them broad access to your project or its resources.

Option A suggests giving the team the "Monitoring Viewer" IAM role. While this role allows users to view metrics, charts, and logs, it grants access to all monitoring data in the project, not just a single chart or dashboard. This could lead to potential overexposure of information, violating the principle of least privilege.

Option B limits access further by assigning the "Dashboard Viewer" IAM role, which restricts users to only viewing dashboards. However, this still requires sharing the entire project ID, and while it’s more limited than Option A, it may still provide more access than necessary depending on how dashboards are structured.

Option C involves using the “Share chart by URL” feature to send a direct link to the chart, which is a good practice in terms of isolating access. However, pairing this with the "Monitoring Viewer" role once again results in broader permissions than necessary, since it allows full visibility into all monitoring data.

Option D—the correct approach—uses the “Share chart by URL” feature for targeted access and pairs it with the "Dashboard Viewer" IAM role. This combination ensures users can view only what’s required. They receive the chart link directly and only get permissions to view dashboards, not to explore other metrics, logs, or settings in the project.

Ultimately, Option D is the most secure and focused solution. It respects the principle of least privilege while achieving the goal of sharing the chart efficiently. This limits risk, minimizes unnecessary exposure, and supports proper governance in cloud resource access management.

Question 3:

Your organization is adopting Site Reliability Engineering (SRE) practices and is focusing on encouraging transparency and continuous improvement. A recent service disruption occurred, and a manager from a different department has asked for a formal explanation to guide remediation efforts. 

To align with SRE values and support learning across teams, what is the most effective way to create and distribute the postmortem?

A. Create a postmortem that outlines root causes, the resolution process, key takeaways, and a prioritized list of corrective actions. Share this only with the requesting manager.
B. Create a postmortem including root causes, resolution steps, insights gained, and prioritized actions. Make it accessible via the engineering team’s document repository.
C. Create a postmortem that identifies root causes, the fix, lessons learned, who was responsible, and what each individual must do. Share it solely with the requesting manager.
D. Create a postmortem that includes root causes, the resolution, lessons learned, individual accountability, and specific action items per person. Publish it on the engineering organization’s internal documentation portal.

Correct Answer: D

Explanation:

In Site Reliability Engineering (SRE), postmortems are essential tools for learning from incidents and building more resilient systems. A proper postmortem should not only analyze what went wrong and how it was fixed but also focus on future prevention and team learning. Transparency and openness are foundational SRE values, especially when it comes to sharing knowledge gained through failure.

Option A outlines a solid structure for the postmortem but falls short in terms of visibility. Limiting access to just one manager restricts the value that could be gained across the organization. SRE principles emphasize shared learning and communal improvement, which this approach does not facilitate.

Option B is more aligned with transparency, as it encourages organization-wide access by publishing the postmortem to a shared portal. However, it does not include accountability at the individual level, which could reduce follow-through on action items. Without clearly assigned responsibilities, important tasks might fall through the cracks.

Option C includes responsibility assignments, which is a good practice. However, its effectiveness is again limited by restricting visibility. Making postmortems available only to a single manager does not encourage organizational learning, which contradicts the SRE goal of fostering a blameless, collaborative learning culture.

Option D combines all the strengths: comprehensive incident details, lessons learned, individual accountability, and broad access. This approach ensures that anyone in the engineering team can benefit from the insights. It also aligns with the concept of blameless postmortems, where the emphasis is on understanding the system failures rather than blaming individuals. By listing responsible parties and action items, there is clarity and accountability, but without creating a culture of fear or punishment.

Publishing the postmortem on the document portal ensures it’s available for reference in future incidents and contributes to organizational memory. Teams can spot patterns over time and improve their operational practices. This is a key enabler of continuous improvement, one of SRE’s fundamental goals.

Therefore, Option D provides the most robust and SRE-aligned approach for handling post-incident reviews.

Question 4:

You are managing applications on a Google Kubernetes Engine (GKE) cluster and are using Stackdriver Kubernetes Engine Monitoring for observability. A new third-party application is being deployed, but it writes logs only to a file located at /var/log/app_messages.log and cannot be modified to change this behavior. 

To ensure these logs are visible in Stackdriver Logging, which method is best suited for capturing and forwarding these logs?

A. Rely on the default configuration of Stackdriver Kubernetes Engine Monitoring to collect the logs.
B. Deploy a Fluentd daemonset on GKE with custom input/output settings to tail the log file and forward it to Stackdriver Logging.
C. Reinstall Kubernetes on Google Compute Engine (GCE) and redeploy the application with a customized logging setup that supports the log file path.
D. Create a script that tails the log file and sends the output to stdout from a sidecar container sharing a volume with the main app container.

Correct Answer: B

Explanation:

In a Kubernetes environment, especially on GKE, observability is key to maintaining operational stability. Stackdriver Logging (now part of Google Cloud Operations) is designed to automatically collect logs written to standard output (stdout) and standard error (stderr) by default. However, when an application writes logs to a specific file within the container—like /var/log/app_messages.log—and cannot be reconfigured, a custom approach is needed.

Option A relies on the default Stackdriver configuration, which typically collects logs from standard Kubernetes sources and container runtime paths. Since the new application writes logs to a non-standard location and cannot be modified, the default configuration will not capture these logs, making this approach ineffective.

Option B is the most robust solution. Fluentd is widely used in Kubernetes for flexible and customizable log collection. By deploying Fluentd as a daemonset, it runs on every node and can be configured to monitor and forward log files from specific paths within the containers. With custom input/output configurations, Fluentd can tail the /var/log/app_messages.log file and send the logs directly to Stackdriver. This ensures seamless integration without modifying the third-party application and supports scalability across your entire cluster.

Option C suggests moving to a self-managed Kubernetes cluster on GCE, which would add significant operational overhead without solving the core problem more effectively than Fluentd. Replacing GKE with a GCE-based Kubernetes environment reduces the benefits of GKE’s managed services and does not offer a simpler path to solving the log collection issue.

Option D proposes using a sidecar container that tails the file and writes logs to stdout. While valid, this introduces more complexity in terms of pod design and resource usage. You would need to manage shared volumes, sidecar synchronization, and scripting for every pod running the application. This method is operationally heavier than simply configuring a Fluentd daemonset to handle everything centrally.

In conclusion, Option B is the most scalable, maintainable, and cloud-native way to collect logs from custom file locations and forward them to Stackdriver Logging, especially for third-party applications that cannot be altered.

Question 5:

You are hosting an application on a Google Cloud virtual machine that uses a custom Debian image. The VM has the Stackdriver Logging agent installed and has been granted the cloud-platform access scope. The application writes its log data using the syslog service. However, when you open the Logs Viewer in the Google Cloud Console, you notice that these syslog messages are not appearing under the "All logs" dropdown. 

What is the very first action you should take to begin diagnosing the problem?

A. Check the Logs Viewer for a test entry generated by the Stackdriver agent.
B. Update the Stackdriver Logging agent to the latest version.
C. Ensure that the VM’s service account includes the monitoring.write scope.
D. Connect to the VM using SSH and run the command: ps ax | grep fluentd.

Correct Answer: D

Explanation:

When troubleshooting missing syslog messages in Google Cloud's Logs Viewer, it’s crucial to confirm whether the Stackdriver (now Cloud Logging) agent—specifically, the Fluentd-based logging agent—is actively running and processing logs on your VM. The most direct approach to this is logging into the instance and checking if Fluentd is operating properly.

Option D, running ps ax | grep fluentd, allows you to verify whether the Fluentd process is currently active. Fluentd serves as the core component of the Stackdriver Logging agent responsible for collecting and forwarding log data, including syslog entries, from your VM to Google Cloud Logging. If this process is missing or not functioning correctly, log forwarding will fail regardless of other configurations, causing logs to be absent in the viewer.

Let’s examine why the other choices are less effective as a first step.

Option A, checking for a test log in the viewer, is helpful for confirming whether the logging agent is generally working, but it assumes that the agent is already running and sending logs. If the agent is not running at all, this test entry won’t be present, leaving you with little insight into the root problem. So, while it’s a useful secondary step, it’s not the most immediate or revealing action to take.

Option B, upgrading the agent, may resolve issues caused by bugs or outdated features, but updating an agent that might not even be running is premature. It's more efficient to confirm whether the agent is operational before deciding if an update is necessary.

Option C, checking the VM’s access scope, is irrelevant in this case. The cloud-platform scope is comprehensive and already includes logging and monitoring permissions, so the lack of visibility in the Logs Viewer is unlikely due to insufficient access.

In summary, the absence of syslog data suggests a potential failure or misconfiguration in the logging pipeline. The most logical first step is to verify whether Fluentd is actually running on the VM by executing a process check. Once confirmed, you can move forward with deeper troubleshooting such as analyzing Fluentd’s configuration or checking its error logs.

Question 6:

Your development team is working on a cloud-based application hosted on Google Kubernetes Engine (GKE). They’ve noticed that the application's performance significantly decreases during peak usage. You’ve been asked to implement a solution that maintains performance under heavy load while optimizing cost. 

What is the most appropriate approach?

A. Configure Horizontal Pod Autoscaler (HPA) based on CPU usage
B. Deploy the application to a larger static node pool
C. Set up Cloud Load Balancing and increase the number of replicas manually
D. Use a vertical pod autoscaler to increase memory allocation for each pod

Correct Answer:  A

Explanation:

This question tests your understanding of scalability and performance optimization using Kubernetes on Google Cloud.

Option A is the best choice because configuring the Horizontal Pod Autoscaler (HPA) allows your application to scale outward based on real-time resource metrics like CPU or memory utilization. When CPU usage increases due to high demand, HPA automatically increases the number of pods to distribute the load, ensuring better performance and optimized costs, since you only use resources as needed.

Option B suggests deploying to a larger static node pool. While it might temporarily solve performance issues, it’s not a cost-efficient or scalable solution. It lacks elasticity and does not adapt dynamically to fluctuating loads.

Option C involves setting up a load balancer and increasing replicas manually, which goes against DevOps principles of automation and self-healing systems. Manual scaling is labor-intensive, error-prone, and doesn’t adapt to unpredictable usage patterns.

Option D, Vertical Pod Autoscaler (VPA), adjusts the resource limits of pods, not the number of pods. While useful in some cases, VPA is less effective when rapid scaling is required during traffic spikes. VPA is also often incompatible with HPA when used together unless configured very carefully.

As a Google Cloud DevOps Engineer, your role is to enable systems that automatically adapt to changes in workload. HPA is designed precisely for this. It's based on metrics server data and Kubernetes API resources, making it a native and optimal solution for dynamic scaling in GKE.

Question 7:

Your CI/CD pipeline is configured in Google Cloud using Cloud Build. Developers recently started noticing longer deployment times after new tests were added. You want to optimize build duration without sacrificing test coverage. What should you do?

A. Use Cloud Build’s parallel execution with separate build steps
B. Disable slower tests and run them only before production deployment
C. Move all testing to developers’ local machines before committing code
D. Create separate pipelines for each microservice and deploy them together

Correct Answer: A

Explanation:

This question assesses your ability to optimize CI/CD pipelines while maintaining test coverage and efficiency.

Option A is the best solution. Cloud Build supports parallel execution of build steps, which can significantly reduce build and test time. You can structure the cloudbuild.yaml file to split the test steps into smaller chunks that run concurrently, such as unit tests, integration tests, and static analysis. This allows you to run comprehensive tests without extending total build time.

Option B may improve speed but at the cost of test coverage. Deferring slower tests to production stages increases risk. DevOps best practices encourage fast feedback loops and early error detection, both of which are compromised here.

Option C shifts responsibility to developers' machines, which introduces inconsistency and removes the centralized validation that CI provides. Also, not all developers have the same local environment, which could result in "works on my machine" problems.

Option D implies deploying all microservices together, which might cause tight coupling and slow deployment overall. The microservices architecture typically favors independent pipelines, but deploying them all together doesn’t help if the tests themselves are the bottleneck.

Using parallelization (Option A) maintains thorough testing and aligns with DevOps principles like speed, automation, and continuous integration. It leverages Cloud Build’s native capabilities for concurrent steps using separate containers, ensuring faster and more scalable pipeline executions.

Question 8:

You are responsible for setting up a deployment pipeline for a containerized application hosted on Google Kubernetes Engine (GKE). You want to ensure that deployments occur with minimal downtime and automatic rollback in case of failures. 

What is the best strategy to implement this?

A. Use a rolling update strategy with readiness probes and enable GKE managed rollout
B. Use a recreate deployment strategy with pre-deployment hooks
C. Deploy using a blue-green deployment and switch manually using a load balancer
D. Deploy using canary releases with traffic split via Cloud Load Balancing

Correct Answer: A

Explanation:

When deploying containerized applications in GKE, minimizing downtime and maintaining availability is a priority. A rolling update strategy is the preferred approach when you want to ensure that pods are updated incrementally while keeping the application available throughout the process.

Rolling updates in GKE allow for gradual replacement of pods, meaning some of the old pods continue serving traffic while new pods are brought up and tested. This strategy supports readiness probes, which ensure that only healthy and fully initialized pods receive traffic. By integrating readiness probes, the Kubernetes controller only routes traffic to new pods once they pass health checks.

Additionally, GKE managed rollout features such as automatic rollback come into play when a deployment fails health checks or doesn’t meet success criteria. If an issue is detected during deployment, the platform can automatically revert to the previous stable version, which significantly enhances resilience.

Let's analyze the other options:

• Option B (Recreate strategy): This involves terminating all existing pods before bringing up new ones. This can cause downtime and is not recommended for production workloads unless necessary.

• Option C (Blue-green deployment): While this can reduce downtime and provides a safe switch between environments, it generally involves manual intervention and more complex setup, including traffic routing.

• Option D (Canary deployment): Canary deployments are excellent for gradually introducing changes but require custom traffic splitting logic, often via Istio or other service meshes, which adds complexity.

Thus, the most practical and native approach for automated, resilient, and low-downtime deployments in GKE is A: Rolling updates with readiness probes and GKE managed rollout.

Question 9:

A development team is struggling with long feedback loops during testing. They want to ensure faster feedback from tests without compromising reliability. 

As a DevOps Engineer, what approach should you recommend to optimize the testing process?

A. Run all tests (unit, integration, and end-to-end) in parallel for faster feedback
B. Run unit tests frequently and delay integration/end-to-end tests to nightly builds
C. Adopt a testing pyramid and emphasize unit tests, with fewer integration and e2e tests
D. Focus solely on end-to-end tests to simulate real user behavior for quality assurance

Correct Answer: C

Explanation:

The testing pyramid is a best practice in DevOps and CI/CD environments. It emphasizes writing a large number of fast unit tests, fewer integration tests, and the least number of end-to-end (E2E) tests. This structure enables faster feedback loops, which are essential for agile development and DevOps practices.

Here's why:

• Unit tests are fast, isolated, and easy to maintain. They provide immediate feedback on whether individual functions or methods behave as expected.

• Integration tests combine multiple components or services and check if they work together. They are slower than unit tests but still faster and more manageable than E2E tests.

• End-to-end tests simulate user workflows. While useful for verifying the entire system, they are slow, flaky, and hard to maintain if overused.

By adopting a testing pyramid (Option C), you ensure that the majority of bugs are caught early in the development cycle, leading to faster and cheaper fixes. This aligns with Google’s Site Reliability Engineering (SRE) principles, which emphasize reliability through automation and early issue detection.

Now let’s look at the incorrect options:

• Option A is risky because running all tests in parallel without prioritization can cause resource contention, longer execution times, and no clarity on what failed where.

• Option B may delay the discovery of integration bugs, potentially impacting release cycles and production reliability.

• Option D focuses only on E2E tests, which are too slow and brittle to support fast iteration or CI/CD pipelines effectively.

Thus, Option C is the best approach: adopt a testing pyramid for optimized speed, reliability, and maintainability.

Question 10:

Your team uses Google Cloud Operations Suite (formerly Stackdriver) to monitor production services. You want to reduce alert fatigue while still ensuring critical issues are addressed promptly. 

Which of the following practices should you implement?

A. Set low thresholds for all metrics to catch potential problems early
B. Create alerts for every warning and informational log entry
C. Implement SLOs and use alerting policies based on error budgets
D. Disable alerting and only use dashboards for manual inspection

Correct Answer: C

Explanation:

In DevOps and SRE practices, alert fatigue is a real concern. When teams receive too many alerts, especially non-critical ones, they may begin ignoring all alerts, potentially missing serious issues. The solution is to focus alerting on what truly matters, and this is where Service Level Objectives (SLOs) and error budgets come into play.

By defining SLOs, teams can set expectations for service performance and availability (e.g., “99.9% availability over 30 days”). These are aligned with Service Level Indicators (SLIs)—quantitative measures like request latency or error rate. Using Google Cloud Monitoring, you can configure alerting policies tied to these metrics.

When a service consumes its error budget too quickly (i.e., it is failing more often than allowed by its SLO), an alert is triggered. This keeps alerts focused on real, user-impacting problems, rather than transient or minor issues. This approach is central to the Google SRE methodology.

Examining the incorrect answers:

• Option A suggests setting low thresholds, which can lead to too many false positives and overwhelm the team.

• Option B is even worse—it treats all logs equally, generating alerts for non-critical events, which defeats the purpose of meaningful alerting.

• Option D suggests turning off alerts entirely. While dashboards are useful, they are passive monitoring tools, and cannot replace proactive alerting.

Therefore, the best choice is C: Implement SLO-based alerting to focus on significant service degradation, reduce noise, and prioritize customer impact.