Professional-Cloud-DevOps-Engineer exam questions for practice in 2026 Updated 208 Questions
Updated Jun-2026 Premium Professional-Cloud-DevOps-Engineer Exam Engine pdf - Download Free Updated 208 Questions
The Professional-Cloud-DevOps-Engineer certification exam consists of multiple-choice and multiple-select questions, and candidates have two hours and thirty minutes to complete the exam. Professional-Cloud-DevOps-Engineer exam is computer-based and can be taken at any Google Cloud Partner testing center or online through the ProctorU platform. Professional-Cloud-DevOps-Engineer exam costs $200, and candidates need to pass with a score of 70% or higher to earn their certification.
NEW QUESTION # 115
You use Cloud Build to build and deploy your application. You want to securely incorporate database credentials and other application secrets into the build pipeline. You also want to minimize the development effort. What should you do?
- A. Use client-side encryption to encrypt the secrets and store them in a Cloud Storage bucket. Store a decryption key in the bucket and grant Cloud Build access to the bucket.
- B. Encrypt the secrets and store them in the application repository. Store a decryption key in a separate repository and grant Cloud Build access to the repository.
- C. Use Cloud Key Management Service (Cloud KMS) to encrypt the secrets and include them in your Cloud Build deployment configuration. Grant Cloud Build access to the KeyRing.
- D. Create a Cloud Storage bucket and use the built-in encryption at rest. Store the secrets in the bucket and grant Cloud Build access to the bucket.
Answer: C
Explanation:
https://cloud.google.com/build/docs/securing-builds/use-encrypted-credentials
NEW QUESTION # 116
You are developing a strategy for monitoring your Google Cloud Platform (GCP) projects in production using Stackdriver Workspaces. One of the requirements is to be able to quickly identify and react to production environment issues without false alerts from development and staging projects. You want to ensure that you adhere to the principle of least privilege when providing relevant team members with access to Stackdriver Workspaces. What should you do?
- A. Grant relevant team members the Project Viewer IAM role on all GCP production projects. Create Slackdriver workspaces inside each project.
- B. Create a new GCP monitoring project, and create a Stackdriver Workspace inside it. Attach the production projects to this workspace. Grant relevant team members read access to the Stackdriver Workspace.
- C. Grant relevant team members read access to all GCP production projects. Create Stackdriver workspaces inside each project.
- D. Choose an existing GCP production project to host the monitoring workspace. Attach the production projects to this workspace. Grant relevant team members read access to the Stackdriver Workspace.
Answer: D
NEW QUESTION # 117
You support a popular mobile game application deployed on Google Kubernetes Engine (GKE) across several Google Cloud regions. Each region has multiple Kubernetes clusters. You receive a report that none of the users in a specific region can connect to the application. You want to resolve the incident while following Site Reliability Engineering practices. What should you do first?
- A. Use Stackdriver Monitoring to check for a spike in CPU or memory usage for the affected region.
- B. Reroute the user traffic from the affected region to other regions that don't report issues.
- C. Use Stackdriver Logging to filter on the clusters in the affected region, and inspect error messages in the logs.
- D. Add an extra node pool that consists of high memory and high CPU machine type instances to the cluster.
Answer: C
NEW QUESTION # 118
Your team is designing a new application for deployment both inside and outside Google Cloud Platform (GCP). You need to collect detailed metrics such as system resource utilization. You want to use centralized GCP services while minimizing the amount of work required to set up this collection system. What should you do?
- A. Install an Application Performance Monitoring (APM) tool in both locations, and configure an export to a central data storage location for analysis.
- B. Instrument the code using a timing library, and publish the metrics via a health check endpoint that is scraped by Stackdriver.
- C. Import the Stackdriver Debugger package, and configure the application to emit debug messages with timing information.
- D. Import the Stackdriver Profiler package, and configure it to relay function timing data to Stackdriver for further analysis.
Answer: A
NEW QUESTION # 119
You manage a critical, user-facing application and have configured a service level objective (SLO) in Cloud Monitoring to track 99% availability over a 30-day rolling window. Recently, a series of minor issues have increased latency, causing the error budget to be consumed at an accelerated rate. You need to be proactively notified when the service is at risk of violating its SLO before the error budget is fully depleted. What should you do?
- A. Configure a log-based metric in Cloud Monitoring that tracks the rate of application error logs. Create an alerting policy on this metric that triggers when the error rate shows a significant anomalous deviation from its historical baseline.
- B. In Metrics Explorer, create an alerting policy based on the average request latency for the application's load balancer. Configure the alert to trigger if latency exceeds a predefined threshold.
- C. Create a custom dashboard in Cloud Monitoring that visualizes the SLO compliance percentage and the remaining error budget. Configure the dashboard to send a scheduled daily report through email.
- D. In Cloud Monitoring, create an alerting policy based on the SLO's error budget burn rate. Configure the alert to trigger when the current consumption rate is projected to exhaust the budget before the end of the 30-day compliance period.
Answer: D
Explanation:
Comprehensive and Detailed 150 to 200 words of Explanation From Google Cloud DevOps guides documents:
In Site Reliability Engineering (SRE), the most effective way to manage an SLO is through Burn Rate Alerting. According to Google Cloud's SRE documentation, a burn rate is the speed at which the error budget is being consumed relative to the SLO's compliance period. Relying on simple threshold alerts (Option D) or manual dashboard checks (Option B) often leads to "alert fatigue" or missed signals because they do not account for the rate of depletion over time.
By creating an alerting policy in Cloud Monitoring specifically based on the burn rate, you can identify
"fast burns" (sudden outages) and "slow burns" (gradual regressions like increased latency). The system calculates the projection; if the current rate of error budget consumption is high enough to exhaust the remaining budget before the end of the 30-day window, it triggers a proactive notification. This allows the team to intervene while they still have a portion of the error budget remaining, effectively preventing an SLO violation rather than merely reacting to one after the fact. This approach aligns perfectly with Google Cloud's recommended practices for automated, data-driven incident prevention.
NEW QUESTION # 120
Your Cloud Run application writes unstructured logs as text strings to Cloud Logging. You want to convert the unstructured logs to JSON-based structured logs. What should you do?
- A. Modify the application to use Cloud Logging software development kit (SDK), and send log entries with a jsonPay10ad field.
- B. Configure the log agent to convert log text payload to JSON payload.
- C. Install the log agent in the Cloud Run container image, and use the log agent to forward logs to Cloud Logging.
- D. A Install a Fluent Bit sidecar container, and use a JSON parser.
Answer: A
Explanation:
Explanation
The correct answer is D. Modify the application to use Cloud Logging software development kit (SDK), and send log entries with a jsonPayload field.
Cloud Logging SDKs are libraries that allow you to write structured logs from your Cloud Run application.
You can use the SDKs to create log entries with a jsonPayload field, which contains a JSON object with the properties of your log entry. The jsonPayload field allows you to use advanced features of Cloud Logging, such as filtering, querying, and exporting logs based on the properties of your log entry1.
To use Cloud Logging SDKs, you need to install the SDK for your programming language, and then use the SDK methods to create and send log entries to Cloud Logging. For example, if you are using Node.js, you can use the following code to write a structured log entry with a jsonPayload field2:
// Imports the Google Cloud client library
const {Logging} = require('@google-cloud/logging');
// Creates a client
const logging = new Logging();
// Selects the log to write to
const log = logging.log('my-log');
// The data to write to the log
const text = 'Hello, world!';
const metadata = {
// Set the Cloud Run service name and revision as labels
labels: {
service_name: process.env.K_SERVICE || 'unknown',
revision_name: process.env.K_REVISION || 'unknown',
},
// Set the log entry payload type and value
jsonPayload: {
message: text,
timestamp: new Date(),
},
};
// Prepares a log entry
const entry = log.entry(metadata);
// Writes the log entry
await log.write(entry);
console.log(`Logged: ${text}`);
Using Cloud Logging SDKs is the best way to convert unstructured logs to structured logs, as it provides more flexibility and control over the format and content of your log entries.
Using a Fluent Bit sidecar container is not a good option, as it adds complexity and overhead to your Cloud Run application. Fluent Bit is a lightweight log processor and forwarder that can be used to collect and parse logs from various sources and send them to different destinations3. However, Cloud Run does not support sidecar containers, so you would need to run Fluent Bit as part of your main container image. This would require modifying your Dockerfile and configuring Fluent Bit to read logs from supported locations and parse them as JSON. This is more cumbersome and less reliable than using Cloud Logging SDKs.
Using the log agent in the Cloud Run container image is not possible, as the log agent is not supported on Cloud Run. The log agent is a service that runs on Compute Engine or Google Kubernetes Engine instances and collects logs from various applications and system components. However, Cloud Run does not allow you to install or run any agents on its underlying infrastructure, as it is a fully managed service that abstracts away the details of the underlying platform.
Storing the password directly in the code is not a good practice, as it exposes sensitive information and makes it hard to change or rotate the password. It also requires rebuilding and redeploying the application each time the password changes, which adds unnecessary work and downtime.
References:
1: Writing structured logs | Cloud Run Documentation | Google Cloud
2: Write structured logs | Cloud Run Documentation | Google Cloud
3: Fluent Bit - Fast and Lightweight Log Processor & Forwarder
Logging Best Practices for Serverless Applications - Google Codelabs
About the logging agent | Cloud Logging Documentation | Google Cloud
Cloud Run FAQ | Google Cloud
NEW QUESTION # 121
You are running an application in a virtual machine (VM) using a custom Debian image. The image has the Stackdriver Logging agent installed. The VM has the cloud-platform scope. The application is logging information via syslog. You want to use Stackdriver Logging in the Google Cloud Platform Console to visualize the logs. You notice that syslog is not showing up in the "All logs" dropdown list of the Logs Viewer. What is the first thing you should do?
- A. SSH to the VM and execute the following commands on your VM: ps ax I grep fluentd
- B. Verify the VM service account access scope includes the monitoring.write scope.
- C. Install the most recent version of the Stackdriver agent.
- D. Look for the agent's test log entry in the Logs Viewer.
Answer: A
Explanation:
https://cloud.google.com/compute/docs/access/service-accounts#associating_a_service_account_to_an_instance
NEW QUESTION # 122
You work for a company that manages highly sensitive user data. You are designing the Google Kubernetes Engine (GKE) infrastructure for your company, including several applications that will be deployed in development and production environments. Your design must protect data from unauthorized access from other applications while minimizing the amount of management overhead required. What should you do?
- A. Create one cluster for each application with separate namespaces for production and development environments.
- B. Create one cluster for the organization with separate namespaces for each application and environment combination.
- C. Create one cluster for each environment (development and production) with each application in its own namespace within each cluster.
- D. Create one cluster for the organization with separate namespaces for each application.
Answer: C
Explanation:
Comprehensive and Detailed Explanation:
For highly sensitive user data, you need to isolate applications while minimizing management overhead. The best practice is:
One GKE cluster per environment (Development and Production) # This provides a clear separation of concerns and avoids security risks from running different environments in the same cluster.
Each application in its own namespace # Namespaces provide logical isolation for different applications within the same cluster, reducing unauthorized access risks.
#Why not other options?
A (Single cluster for org with multiple namespaces for apps & envs)## Bad security practice because mixing production and development in the same cluster increases the risk of privilege escalation.
C (Single cluster for org with namespaces per app)## Still mixes development and production in the same cluster, violating isolation requirements for sensitive data.
D (One cluster per app)## High operational overhead; unnecessary complexity for small to medium-scale deployments.
#Official Reference:
GKE Multi-Tenancy Best Practices
GKE Security Hardening
NEW QUESTION # 123
You are the Site Reliability Engineer responsible for managing your company's data services and products.
You regularly navigate operational challenges, such as unpredictable data volume and high cost, with your company's data ingestion processes. You recently learned that a new data ingestion product will be developed in Google Cloud. You need to collaborate with the product development team to provide operational input on the new product. What should you do?
- A. When the new product is used by at least one internal customer in production, share error logs and monitoring metrics with the product development team.
- B. Deploy the prototype product in a test environment, run a load test, and share the results with the product development team.
- C. When the initial product version passes the quality assurance phase and compliance assessments, deploy the product to a staging environment. Share error logs and performancemetrics with the product development team.
- D. Review the design of the product with the product development team to provide feedback early in the design phase.
Answer: D
Explanation:
The correct answer is D. Review the design of the product with the product development team to provide feedback early in the design phase.
According to the Google Cloud DevOps best practices, a Site Reliability Engineer (SRE) should collaborate with the product development team from the beginning of the product lifecycle, not just after the product is deployed or tested. This way, the SRE can provide operational input on the product design, such as scalability, reliability, security, and cost efficiency. The SRE can also help define service level objectives (SLOs) and service level indicators (SLIs) for the product, as well as monitoring and alerting strategies. By collaborating early and often, the SRE and the product development team can ensure that the product meets the operational requirements and expectations of the customers.
NEW QUESTION # 124
Your application images are built and pushed to Google Container Registry (GCR). You want to build an automated pipeline that deploys the application when the image is updated while minimizing the development effort. What should you do?
- A. Use a custom builder in Cloud Build to trigger Jenkins pipeline.
- B. Use Cloud Pub/Sub to trigger a custom deployment service running in Google Kubernetes Engine (GKE).
- C. Use Cloud Build to trigger a Spinnaker pipeline.
- D. Use Cloud Pub/Sub to bigger a Spinnaker pipeline.
Answer: B
NEW QUESTION # 125
You created a Stackdriver chart for CPU utilization in a dashboard within your workspace project. You want to share the chart with your Site Reliability Engineering (SRE) team only. You want to ensure you follow the principle of least privilege. What should you do?
- A. Click "Share chart by URL" and provide the URL to the SRE team. Assign the SRE team the Monitoring Viewer IAM role in the workspace project.
- B. Click "Share chart by URL" and provide the URL to the SRE team. Assign the SRE team the Dashboard Viewer IAM role in the workspace project.
- C. Share the workspace Project ID with the SRE team. Assign the SRE team the Monitoring Viewer IAM role in the workspace project.
- D. Share the workspace Project ID with the SRE team. Assign the SRE team the Dashboard Viewer IAM role in the workspace project.
Answer: D
NEW QUESTION # 126
You are creating and assigning action items in a postmodern for an outage. The outage is over, but you need to address the root causes. You want to ensure that your team handles the action items quickly and efficiently. How should you assign owners and collaborators to action items?
- A. Assign multiple owners for each item to guarantee that the team addresses items quickly
- B. Assign the team lead as the owner for all action items because they are in charge of the SRE team.
- C. Assign one owner for each action item and any necessary collaborators.
- D. Assign collaborators but no individual owners to the items to keep the postmortem blameless.
Answer: C
Explanation:
https://devops.com/when-it-disaster-strikes-part-3-conducting-a-blameless-post-mortem/
NEW QUESTION # 127
Your application images are built using Cloud Build and pushed to Google Container Registry (GCR). You want to be able to specify a particular version of your application for deployment based on the release version tagged in source control. What should you do when you push the image?
- A. Use Cloud Build to include the release version tag in the application image.
- B. Reference the image digest in the source control tag.
- C. Supply the source control tag as a parameter within the image name.
- D. Use GCR digest versioning to match the image to the tag in source control.
Answer: C
Explanation:
Explanation
https://cloud.google.com/container-registry/docs/pushing-and-pulling
NEW QUESTION # 128
You have a pool of application servers running on Compute Engine. You need to provide a secure solution that requires the least amount of configuration and allows developers to easily access application logs for troubleshooting. How would you implement the solution on GCP?
- A. * Deploy the Stackdriver logging agent to the application servers.
* Give the developers the IAM Logs Private Logs Viewer role to access Stackdriver and view logs. - B. * Deploy the Stackdriver monitoring agent to the application servers.
* Give the developers the IAM Monitoring Viewer role to access Stackdriver and view metrics. - C. * Deploy the Stackdriver logging agent to the application servers.
* Give the developers the IAM Logs Viewer role to access Stackdriver and view logs. - D. * Install the gsutil command line tool on your application servers.
* Write a script using gsutil to upload your application log to a Cloud Storage bucket, and then schedule it to run via cron every 5 minutes.
* Give the developers IAM Object Viewer access to view the logs in the specified bucket.
Answer: C
Explanation:
https://cloud.google.com/logging/docs/audit#access-control
NEW QUESTION # 129
Your team is preparing to launch a new API in Cloud Run. The API uses an OpenTelemetry agent to send distributed tracing data to Cloud Trace to monitor the time each request takes. The team has noticed inconsistent trace collection. You need to resolve the issue. What should you do?
- A. Increase the CPU limit in Cloud Run from 2 to 4.
- B. Use an HTTP health check.
- C. Configure CPU to be always-allocated.
- D. Configure CPU to be allocated only during request processing.
Answer: C
Explanation:
Comprehensive and Detailed Explanation:
Cloud Run scales down to zero when idle. If CPU is allocated only during request processing, background telemetry tasks (like OpenTelemetry traces) may not complete before the container shuts down.
Solution: Configure CPU to be always-allocated # This ensures that background tasks (like trace exports) continue even when the service is idle.
#Why not other options?
A (Increasing CPU to 4 cores)## More CPU won't help if traces are being cut off due to shutdown.
B (Using an HTTP health check)## Doesn't affect OpenTelemetry traces.
C (CPU only during request processing)## This causes the problem because traces need CPU after request completion to be exported.
#Official Reference:
Cloud Run Tracing with OpenTelemetry
Cloud Run Always-Allocated CPU
NEW QUESTION # 130
You manage several production systems that run on Compute Engine in the same Google Cloud Platform (GCP) project. Each system has its own set of dedicated Compute Engine instances. You want to know how must it costs to run each of the systems. What should you do?
- A. Enrich all instances with metadata specific to the system they run. Configure Stackdriver Logging to export to BigQuery, and query costs based on the metadata.
- B. In the Google Cloud Platform Console, use the Cost Breakdown section to visualize the costs per system.
- C. Assign all instances a label specific to the system they run. Configure BigQuery billing export and query costs per label.
- D. Name each virtual machine (VM) after the system it runs. Set up a usage report export to a Cloud Storage bucket. Configure the bucket as a source in BigQuery to query costs based on VM name.
Answer: C
Explanation:
https://cloud.google.com/billing/docs/how-to/export-data-bigquery
NEW QUESTION # 131
You support a web application that is hosted on Compute Engine. The application provides a booking service for thousands of users. Shortly after the release of a new feature, your monitoring dashboard shows that all users are experiencing latency at login. You want to mitigate the impact of the incident on the users of your service. What should you do first?
- A. Roll back the recent release.
- B. Upsize the virtual machines running the login services.
- C. Review the Stackdriver monitoring.
- D. Deploy a new release to see whether it fixes the problem.
Answer: B
NEW QUESTION # 132
Your team of Infrastructure DevOps Engineers is growing, and you are starting to use Terraform to manage infrastructure. You need a way to implement code versioning and to share code with other team members. What should you do?
- A. Store the Terraform code in a network shared folder with child folders for each version release. Ensure that everyone works on different files.
- B. Store the Terraform code in a shared Google Drive folder so it syncs automatically to every team member's computer. Organize files with a naming convention that identifies each new version.
- C. Store the Terraform code in a version-control system. Establish procedures for pushing new versions and merging with the master.
- D. Store the Terraform code in a Cloud Storage bucket using object versioning. Give access to the bucket to every team member so they can download the files.
Answer: C
NEW QUESTION # 133
You need to deploy a new service to production. The service needs to automatically scale using a Managed Instance Group (MIG) and should be deployed over multiple regions. The service needs a large number of resources for each instance and you need to plan for capacity. What should you do?
- A. Deploy the service in one region and use a global load balancer to route traffic to this region.
- B. Use the n2-highcpu-96 machine type in the configuration of the MIG.
- C. Validate that the resource requirements are within the available quota limits of each region.
- D. Monitor results of Stackdriver Trace to determine the required amount of resources.
Answer: C
Explanation:
https://cloud.google.com/compute/quotas#understanding_quotas
https://cloud.google.com/compute/quotas
NEW QUESTION # 134
You use Terraform to manage an application deployed to a Google Cloud environment The application runs on instances deployed by a managed instance group The Terraform code is deployed by using a CI/CD pipeline When you change the machine type on the instance template used by the managed instance group, the pipeline fails at the terraform apply stage with the following error message
You need to update the instance template and minimize disruption to the application and the number of pipeline runs What should you do?
- A. Set the create_bef ore_destroy meta-argument to true in the lifecycle block on the instance template
- B. Add a new instance template update the managed instance group to use the new instance template and delete the old instance template
- C. Remove the managed instance group from the Terraform state file update the instance template and reimport the managed instance group.
- D. Delete the managed instance group and recreate it after updating the instance template
Answer: A
Explanation:
Explanation
The best option for updating the instance template and minimizing disruption to the application and the number of pipeline runs is to set the create_before_destroy meta-argument to true in the lifecycle block on the instance template. The create_before_destroy meta-argument is a Terraform feature that specifies that a new resource should be created before destroying an existing one during an update. This way, you can avoid downtime and errors when updating a resource that is in use by another resource, such as an instance template that is used by a managed instance group. By setting the create_before_destroy meta-argument to true in the lifecycle block on the instance template, you can ensure that Terraform creates a new instance template with the updated machine type, updates the managed instance group to use the new instance template, and then deletes the old instance template.
NEW QUESTION # 135
You are running an application in a virtual machine (VM) using a custom Debian image. The image has the Stackdriver Logging agent installed. The VM has the cloud-platform scope. The application is logging information via syslog. You want to use Stackdriver Logging in the Google Cloud Platform Console to visualize the logs. You notice that syslog is not showing up in the "All logs" dropdown list of the Logs Viewer. What is the first thing you should do?
- A. SSH to the VM and execute the following commands on your VM: ps ax I grep fluentd
- B. Verify the VM service account access scope includes the monitoring.write scope.
- C. Install the most recent version of the Stackdriver agent.
- D. Look for the agent's test log entry in the Logs Viewer.
Answer: A
NEW QUESTION # 136
You are creating Cloud Logging sinks to export log entries from Cloud Logging to BigQuery for future analysis Your organization has a Google Cloud folder named Dev that contains development projects and a folder named Prod that contains production projects Log entries for development projects must be exported to dev_dataset. and log entries for production projects must be exported to prod_datasetYou need to minimize the number of log sinks created and you want to ensure that the log sinks apply to future projects What should you do?
- A. Create two aggregated log sinks at the organization level, and filter by project ID
- B. Create a single aggregated log sink at the organization level.
- C. Create an aggregated Iog sink in the Dev and Prod folders
- D. Create a log sink in each project
Answer: B
NEW QUESTION # 137
You are responsible for creating and modifying the Terraform templates that define your Infrastructure. Because two new engineers will also be working on the same code, you need to define a process and adopt a tool that will prevent you from overwriting each other's code. You also want to ensure that you capture all updates in the latest version. What should you do?
- A. * Store your code as text files in Google Drive in a defined folder structure that organizes the files.
* At the end of each day, confirm that all changes have been captured in the files within the folder structure and create a new .zip archive with a predefined naming convention.
* Upload the .zip archive to a versioned Cloud Storage bucket and accept it as the latest version. - B. * Store your code in a Git-based version control system.
* Establish a process that allows developers to merge their own changes at the end of each day.
* Package and upload code lo a versioned Cloud Storage bucket as the latest master version. - C. * Store your code as text files in Google Drive in a defined folder structure that organizes the files.
* At the end of each day. confirm that all changes have been captured in the files within the folder structure.
* Rename the folder structure with a predefined naming convention that increments the version. - D. * Store your code in a Git-based version control system.
* Establish a process that includes code reviews by peers and unit testing to ensure integrity and functionality before integration of code.
* Establish a process where the fully integrated code in the repository becomes the latest master version.
Answer: B
NEW QUESTION # 138
Your company runs services by using multiple globally distributed Google Kubernetes Engine (GKE) clusters Your operations team has set up workload monitoring that uses Prometheus-based tooling for metrics alerts:
and generating dashboards This setup does not provide a method to view metrics globally across all clusters You need to implement a scalable solution to support global Prometheus querying and minimize management overhead What should you do?
- A. Configure Prometheus hierarchical federation for centralized data access
- B. Configure Google Cloud Managed Service for Prometheus
- C. Configure workload metrics within Cloud Operations for GKE
- D. Configure Prometheus cross-service federation for centralized data access
Answer: B
Explanation:
Explanation
The best option for implementing a scalable solution to support global Prometheus querying and minimize management overhead is to use Google Cloud Managed Service for Prometheus. Google Cloud Managed Service for Prometheus is a fully managed service that allows you to collect, query, and visualize metrics from your GKE clusters using Prometheus-based tooling. You can use Google Cloud Managed Service for Prometheus to query metrics across multiple clusters and regions using a global view. You can also use Google Cloud Managed Service for Prometheus to integrate with other Google Cloud services, such as Cloud Monitoring, Cloud Logging, and BigQuery. By using Google Cloud Managed Service for Prometheus, you can avoid managing and scaling your own Prometheus servers and focus on your application performance.
NEW QUESTION # 139
Your company operates in a highly regulated domain that requires you to store all organization logs for seven years You want to minimize logging infrastructure complexity by using managed services You need to avoid any future loss of log capture or stored logs due to misconfiguration or human error What should you do?
- A. Use Cloud Logging to configure an export sink at each project level to export all logs into a BigQuery dataset
- B. Use Cloud Logging to configure an aggregated sink at the organization level to export all logs into a BigQuery dataset
- C. Use Cloud Logging to configure an export sink at each project level to export all logs into Cloud Storage with a seven-year retention policy and Bucket Lock
- D. Use Cloud Logging to configure an aggregated sink at the organization level to export all logs into Cloud Storage with a seven-year retention policy and Bucket Lock
Answer: D
NEW QUESTION # 140
......
Topics of Google Professional Cloud DevOps Engineer Exam
Candidates must know the exam topics before they start preparation because it will help them in hitting the core. Our Professional Cloud DevOps Engineer Dumps will include the following topics:
Applying site reliability engineering principles to a service
- Balance change, velocity, and reliability of the service:
- Agree to consequences of not meeting the error budget
- Define SLOs and understand SLAs
- Discover SLIs (availability, latency, etc.)
- Toil automation
Building and implementing CI/CD pipelines for a service
- Deployment strategies with Cloud Build, Spinnaker
- Testing a new version with Spinnaker
- Deployment to hybrid and multi-cloud environments with Anthos, Spinnaker, Kubernetes
- Design CI/CD pipelines:
- Configure deployment processes (e.g., approval flows)
- CI/CD pipeline triggers with Cloud Source Repositories, Cloud Build GitHub App, Cloud Pub/Sub
Implementing service monitoring strategies
- Collecting logs from Compute Engine, GKE with Stackdriver Logging, Fluentd
- Selecting the options for logging export
- Viewing logs in the GCP Console
- Use metric explorer for ad hoc metric analysis
- Collecting metrics from Compute Engine
- Collecting GKE/Kubernetes metrics
- Manage application logs:
- Using basic vs. advanced logging filters
- Implementing a project-level / org-level export
- Sending application logs directly to Stackdriver API with Stackdriver Logging
- Enabling data access logs (e.g., Cloud Audit Logs)
Optimizing service performance
- identify resource costs
- Troubleshoot issues with the image/OS
- Interpret service mesh telemetry
- Consider network pricing
- Utilize Stackdriver Trace/Profiler to profile performance characteristics
- Manage preemptible VMs
- Work with committed-use discounts
- Utilize Stackdriver to identify cloud resource utilization
- Identify service performance issues:
- Evaluate and understand user impact (Stackdriver Service Monitoring for App Engine, Istio)
- Troubleshoot network issues (e.g., VPC flow logs, firewall logs, latency, view network details)
- Identify resource utilization levels
Managing service incidents
- Define roles (incident commander, communication lead, operations lead)
- Establish communications channels (email, IRC, Hangouts, Slack, phone, etc.)
- Identify probable causes of service failure
- Manage stakeholder relationships
- Scaling response team and delegation
- Record major changes in incident state (When mitigated? When all clear? etc.)
- Coordinate roles and implement communication channels during a service incident:
- Avoid exhaustion/burnout
- Provide regular status updates, internal and external
- Rotate/hand over roles
- Handle requests for impact assessment
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