Google Professional Cloud DevOps Engineer Exam Dumps & Practice Test Questions
Question No 1:
Your team is working on a cloud-native application that will be deployed on Google Kubernetes Engine (GKE). One of the critical requirements is to set up centralized monitoring to track key application-level metrics like request volume, response latency, and error frequency. The chosen solution must use native Google Cloud services for seamless integration, dependability, and low maintenance effort.
The objective is to gather, export, and display these metrics efficiently while keeping overhead minimal.
Which solution best meets these needs following best practices on Google Cloud Platform?
A. Send application-generated custom metrics directly to the Stackdriver Monitoring API and use Stackdriver to visualize them
B. Use Cloud Pub/Sub libraries to send application metrics to various topics, then aggregate and observe them in Stackdriver
C. Integrate OpenTelemetry libraries within the application, configure Stackdriver as the destination, and view metrics in Stackdriver
D. Output all application metrics as log entries, collect them via the Stackdriver logging agent, and view them in Stackdriver
Correct Answer: C. Integrate OpenTelemetry libraries within the application, configure Stackdriver as the destination, and view metrics in Stackdriver
Explanation
To achieve efficient monitoring of application metrics within GKE, the most streamlined and scalable option is to use OpenTelemetry. This open-source framework allows developers to instrument their applications and export telemetry data such as metrics, logs, and traces in a standardized way.
By embedding OpenTelemetry libraries in the application and setting Stackdriver (now part of Cloud Monitoring) as the metrics export target, you gain tight integration with Google Cloud’s native observability stack. This enables out-of-the-box dashboards, alerting, and visualization without significant configuration or maintenance. It follows cloud-native best practices and provides flexibility for future growth.
Option A introduces more manual effort and lacks built-in standardization.
Option B misuses Cloud Pub/Sub, which is designed for messaging rather than metrics collection.
Option D relies on parsing logs for metrics, which is inefficient and less real-time.
OpenTelemetry, therefore, represents the optimal path for exporting and monitoring application metrics in GKE, providing a robust and automated solution.
Question No 2:
You are managing a critical production application hosted on a single virtual machine (VM) in Google Compute Engine. Occasionally, this VM becomes unresponsive, and you currently resolve the issue by manually deleting and recreating the instance using a machine image. This manual process is time-consuming and negatively affects productivity. To increase reliability and reduce manual work, you want to follow Site Reliability Engineering (SRE) principles, focusing on automation and system self-repair.
Which action should you take to make the service more reliable and reduce manual effort?
A. Report the issue to the development team by filing a bug so they can investigate the crashes
B. Configure a Managed Instance Group (MIG) with one VM and enable health checks to automatically replace failed instances
C. Set up a Load Balancer in front of the VM and configure health checks to ensure availability
D. Create a monitoring dashboard and configure SMS alerts to notify the team when the VM crashes
Correct Answer: B. Configure a Managed Instance Group (MIG) with one VM and enable health checks to automatically replace failed instances
Explanation
The best solution to improve reliability and eliminate manual recovery is to use a Managed Instance Group (MIG). MIGs provide automatic VM provisioning, scaling, and healing. When configured with a health check, a MIG will continuously monitor the VM. If the instance becomes unhealthy, it will be automatically deleted and recreated based on a predefined template or image.
This approach adheres to SRE principles by minimizing human intervention and automating repetitive operations. It reduces operational toil and ensures that critical services can self-heal without needing manual oversight.
Option A (filing a bug) might address the root issue eventually but doesn't solve the immediate reliability concern.
Option C (adding a Load Balancer) is suitable when multiple backends exist and does not provide auto-recovery for a single VM.
Option D (manual SMS alerts) still depends on human reaction, which goes against the principle of automation.
MIGs deliver the automation and resilience needed to maintain uptime and efficiency, even for single-instance scenarios.
Question No 3:
You are overseeing a CI/CD pipeline that handles automated building and deployment of your application. This system requires secure handling of confidential elements such as API tokens, database credentials, and keys for external services. Your objective is to manage these secrets safely, avoid embedding them in code, and enable straightforward rotation when necessary.
Which of the following approaches offers the most secure and efficient method to manage secrets while fulfilling both operational and security demands?
A. Have developers enter secrets manually during each build, ensuring they do not save them to local storage.
B. Store secrets in a configuration file located in the source code repository and restrict access to a small number of developers.
C. Use Cloud Storage to keep secrets encrypted with Cloud KMS and configure the CI/CD pipeline with IAM roles to decrypt these as needed.
D. Encrypt secrets and include them in the source repository, while keeping the decryption keys in a separate repository accessible to the pipeline.
Correct Answer: C
Explanation:
The optimal and secure solution is to store secrets in Cloud Storage, encrypt them using Cloud Key Management Service (KMS), and provide decryption access to the CI/CD pipeline through IAM roles.
Using Cloud KMS enables centralized and auditable key control, ensuring secrets are encrypted during storage and transmission. Storing secrets outside the code base aligns with the 12-factor app methodology, encouraging separation of configuration from application logic.
The pipeline should be assigned a service account with limited IAM permissions that allow it to access Cloud Storage and decrypt data using KMS. This method allows for detailed access control and audit logging, enhancing security.
This setup also supports efficient secret rotation. If a secret becomes compromised, it can be replaced and re-encrypted with minimal disruption, without changing the application itself. Cloud KMS simplifies key rotation and integrates well with logging tools to track access events.
Let’s consider the other options:
Option A is risky and inefficient as it depends on human input and increases the likelihood of errors.
Option B places sensitive data in version control, which can expose secrets.
Option D adds unnecessary complexity and risk by using dual repositories, making key management harder.
Utilizing Cloud Storage with Cloud KMS and IAM is a secure, cloud-native solution that suits modern CI/CD workflows.
Question No 4:
You are the designated Communications Lead during a major production outage affecting customer-facing services. The cause of the issue is still unknown, and no recovery timeline has been established. Internal stakeholders are frequently requesting updates, and customers are seeking clarity on the situation.
What is the most effective course of action to manage communication and preserve transparency and trust during this incident?
A. Concentrate on updating internal stakeholders every 30 minutes and include a committed timeline for the next update.
B. Issue regular updates to both internal and external parties and always specify when the next update will occur.
C. Assign someone else on the incident response team to handle internal updates so you can respond to customers directly.
D. Forward internal stakeholder messages to the Incident Commander and focus your attention on communicating with customers.
Correct Answer: B
Explanation:
During critical incidents, effective and structured communication is essential. In the SRE framework, communication is not secondary—it’s a central component of incident management.
The recommended approach is to provide consistent updates to everyone involved, including internal teams and customers. By committing to a “next update” timeframe in every message, you foster transparency and reduce the need for individual follow-ups, which lowers confusion and tension.
This method adheres to core SRE communication principles:
Transparency: Even if progress is slow, sharing what is known and being honest about uncertainties builds trust.
Consistency: Providing regular, uniform updates ensures that all parties are on the same page and prevents conflicting information.
Commitment: Setting expectations around timing demonstrates control and attentiveness.
Options A, C, and D focus communication too narrowly and risk leaving one group uninformed. Dividing communication between multiple people without a unified message may lead to inconsistencies. While delegation can be useful, the central communication strategy must remain coherent and centrally managed.
The ideal practice includes using shared resources like status dashboards or incident logs and sticking to an update schedule. This instills confidence and upholds the professional standards expected in an SRE-driven incident response.
Question No 5:
Your development team uses Google Cloud Build for CI/CD automation. As part of your deployment process, you need to use the kubectl builder in Cloud Build to deploy newly built container images to a Google Kubernetes Engine (GKE) cluster. To accomplish this, you must ensure that kubectl within the Cloud Build environment can authenticate and interact with your GKE cluster. You aim to implement this with minimal development and operational overhead.
What is the most efficient and recommended way to allow authentication from Cloud Build to GKE?
A. Grant the Cloud Build service account the Container Developer IAM role
B. Specify the Container Developer role directly in the cloudbuild.yaml configuration
C. Create a new custom service account with the Container Developer role and configure Cloud Build to use it
D. Add a step in your cloudbuild.yaml file to manually obtain and configure credentials for kubectl authentication
Correct Answer: A. Grant the Cloud Build service account the Container Developer IAM role
Explanation:
To enable kubectl commands in Cloud Build to interact with a GKE cluster, authentication through the Google Cloud identity system is required. The most efficient and Google-recommended method is assigning the Container Developer IAM role to the Cloud Build service account. This provides the necessary permissions to access GKE clusters, manage workloads, and retrieve credentials via gcloud.
Cloud Build automatically uses its default service account for executing pipeline steps. When this account is granted the right IAM role, it can seamlessly run commands such as gcloud container clusters get-credentials, allowing kubectl to connect to the desired GKE cluster without additional setup.
Option A requires the least configuration and effort while staying aligned with best practices for secure, integrated role-based access.
Option B is not valid because IAM permissions cannot be defined inside the cloudbuild.yaml file.
Option C introduces unnecessary complexity by requiring a custom service account and additional setup in Cloud Build, without any added advantage.
Option D involves manual credential handling, which increases operational overhead and reduces security by bypassing native identity services.
Thus, A is the best and most efficient choice.
Question No 6:
You manage an application on Google Cloud Platform that uses in-memory caching for fast product data access. When a cache miss occurs—meaning the product data is not found in cache—a log entry is generated and stored in Google Cloud Logging.
You want to visualize the frequency of cache misses over time to better understand and improve cache performance.
What is the most effective and scalable method to accomplish this using GCP-native tools?
A. Connect Google Cloud Logging as a data source in Google Data Studio and apply filters to extract only the cache miss entries
B. Use Cloud Profiler to identify and visualize the cache miss patterns based on logged events
C. Create a logs-based metric from the cache miss entries in Google Cloud Logging, and then build a dashboard in Cloud Monitoring to visualize the data over time
D. Export the logs to BigQuery using a sink, then run scheduled queries to filter cache miss events and use a new table for visualization
Correct Answer: C. Create a logs-based metric from the cache miss entries in Google Cloud Logging, and then build a dashboard in Cloud Monitoring to visualize the data over time
Explanation:
The most effective and scalable approach to monitor cache miss frequency is to create a logs-based metric from relevant log entries in Google Cloud Logging, then visualize the metric in Cloud Monitoring.
Logs-based metrics allow you to define a filter for specific log messages, such as those that indicate a cache miss. These filtered logs are converted into countable metrics that can be charted over time.
Once the metric is created, you can use Cloud Monitoring to build a dashboard displaying the frequency of cache misses across different time intervals (e.g., hourly, daily). This enables real-time analysis and historical insights into cache performance without exporting data to other systems.
Option A lacks integration with metric systems and is not ideal for operational dashboards.
Option B is incorrect, as Cloud Profiler is designed for analyzing application performance (like CPU or memory usage), not for monitoring log events.
Option D, while feasible, is more complex. It involves managing log exports, writing queries, maintaining datasets, and possibly incurring higher costs.
Therefore, C is the most practical and scalable solution for visualizing cache miss trends using GCP's built-in observability tools.
Question No 7:
You are planning to launch a service on Google Cloud Platform (GCP) that requires significant resources per instance. This service needs to be highly available and capable of scaling automatically based on demand. To achieve this, you intend to use a Managed Instance Group (MIG) spread across multiple regions. Given the high resource consumption, effective planning is essential to ensure the deployment works seamlessly.
What action should you take to ensure that your service can be deployed successfully across regions and meet the resource requirements?
A. Configure the MIG to use the n1-highcpu-96 machine type to meet performance needs
B. Monitor Stackdriver Trace to evaluate and calculate the appropriate resource sizing
C. Verify that your resource demands are within quota limits in each target region
D. Deploy the service to a single region and utilize a global load balancer to direct traffic
Correct Answer: C. Verify that your resource demands are within quota limits in each target region
Explanation:
When planning a multi-regional deployment using Managed Instance Groups, one of the most important steps is confirming that each region has sufficient quota to support your selected instance types and scaling needs. Quotas in Google Cloud control the maximum resources you can consume per region, such as vCPUs and memory. If your deployment exceeds these limits, the MIG may fail to scale as required.
Although selecting a powerful machine type like n1-highcpu-96 (Option A) may fulfill performance expectations, it is ineffective if your quota does not permit the use of such instances at scale. Stackdriver Trace (Option B) is useful for diagnosing performance and latency issues but does not address quota availability or capacity limits.
Deploying to a single region with a global load balancer (Option D) may help distribute traffic globally but does not meet the requirement for a true multi-regional deployment, nor does it solve issues related to regional quotas.
The best strategy is to check quota availability in each region where you plan to deploy. This ensures your infrastructure can scale as intended without being constrained by limitations. If necessary, you can request quota increases through the Cloud Console to meet your needs.
Question No 8:
In the TOGAF standard, architectural artifacts are essential for expressing different facets of an enterprise's architecture. These artifacts aid in communication and understanding across stakeholders. Among the various artifact types defined by TOGAF,
Which one specifically serves as a representation tailored to a related group of concerns and stakeholder interests?
A. Matrix
B. Diagram
C. Architecture View
D. Catalog
Correct Answer: C. Architecture View
Explanation:
Within the TOGAF framework, an architecture view is designed to focus on a specific set of concerns that are relevant to a stakeholder or stakeholder group. These views are composed using architectural models and are constructed based on architecture viewpoints, which define how a view should be built, what it should represent, and the modeling techniques to apply.
For instance, a security viewpoint might establish the modeling standards to describe data protection or access control. The resulting view would then illustrate these aspects, addressing the concerns of security professionals or compliance officers.
Other artifact types in TOGAF play different roles:
Catalogs organize lists of elements like applications or capabilities in a structured format.
Matrices display relationships between components, such as mapping business processes to applications.
Diagrams visually depict systems or components but don't encompass the broader stakeholder-driven focus that defines an architecture view.
Therefore, architecture views serve as the central method in TOGAF for addressing specific concerns by offering a comprehensive representation that aligns architectural models with stakeholder needs.
Question No 9:
You are managing a CI/CD pipeline using Google Cloud Build that automatically creates Docker images and pushes them to Docker Hub. The source code is maintained in Git, and the build process is triggered on each code commit.
After making changes to the cloudbuild.yaml file, the pipeline stopped generating build artifacts. No new Docker images are being pushed, and there are no visible errors—indicating the build process is failing silently.
Following Site Reliability Engineering (SRE) principles such as automation, observability, and proactive diagnosis, you aim to resolve this issue without relying on manual processes or introducing instability.
What is the best action to take to identify and resolve the configuration problem in line with SRE best practices?
A. Disable the CI pipeline and revert to manually building and pushing the artifacts
B. Modify the CI pipeline to push Docker images to Google Container Registry instead of Docker Hub
C. Upload the updated configuration YAML file to Cloud Storage and use Cloud Error Reporting to debug
D. Run a Git diff (comparison) between the current and previous versions of the Cloud Build configuration file to identify and fix the issue
Correct Answer: D
Explanation:
The most efficient approach to diagnosing this issue is by running a Git diff between the current and last working version of the cloudbuild.yaml file. This comparison allows you to pinpoint any recent syntax errors or misconfigurations that could be responsible for the pipeline's failure.
This method aligns well with SRE principles. It relies on automation and observability rather than reactive or manual solutions. A Git diff offers visibility into changes without needing to dismantle or alter functioning systems, making it a non-invasive way to troubleshoot.
Choosing manual builds and pushes (A) contradicts the SRE emphasis on automation and reliability. Altering the artifact destination to Google Container Registry (B) does not address the root problem and introduces unnecessary change. Uploading the YAML file to Cloud Storage and relying on Cloud Error Reporting (C) adds unnecessary complexity and does not directly address the misconfiguration.
By sticking to version control best practices and isolating the changes that caused the issue, you maintain the consistency and resilience of the CI/CD workflow. This proactive and precise diagnostic step helps restore pipeline functionality without undermining stability or introducing new variables.
Question No 10:
Your development team is about to deploy a new version of your web application hosted on Google Kubernetes Engine (GKE). The team wants to ensure a smooth release, allowing for instant fallback if unexpected issues arise during rollout. They also want to validate the new version before exposing it to all users.
Which deployment technique best fulfills these objectives?
A. Gradually replacing pods without downtime using a sequential rollout
B. Deploying the updated version to a separate environment, then switching user traffic after verification
C. Updating all pods simultaneously to deliver the new version as fast as possible
D. Manually logging into each pod and applying updates individually
Correct Answer:
B. Deploying the updated version to a separate environment, then switching user traffic after verification
Explanation:
The best practice for minimizing risk during application deployments—especially on platforms like GKE—is to use a blue/green deployment strategy. This approach involves creating two distinct environments: the existing production environment (blue) and a new staging environment (green) with the updated version of the application.
Once the green environment is deployed and fully validated through testing or limited user exposure, traffic is switched over from blue to green. This switch is often instantaneous, allowing for a seamless experience. If issues are detected, you can quickly roll back by redirecting traffic back to the stable blue environment.
Here’s how the options compare:
Option A (sequential rollout): Refers to a rolling update, which is slower and doesn't offer instant rollback. If an error is detected mid-rollout, recovering can be more complex.
Option B (blue/green): Offers zero-downtime deployment and rapid rollback, making it ideal for mission-critical apps.
Option C (simultaneous update): Represents a "big bang" release—fast but risky, with high potential for total failure.
Option D (manual update): Is inefficient, prone to human error, and not scalable.
For a Google Cloud DevOps Engineer, mastering deployment patterns like blue/green and canary deployments is essential for ensuring high availability, minimizing disruptions, and improving release confidence.
