Professional-Cloud-DevOps-Engineer Actual Questions 100% Same Braindumps with Actual Exam!
Professional-Cloud-DevOps-Engineer Study Material, Preparation Guide and PDF Download
Google Cloud Certified Professional Cloud DevOps Engineer exam is designed to test the skills and knowledge of professionals who can help organizations improve their software delivery pipelines and build reliable, scalable, and secure applications on Google Cloud Platform (GCP). Google Cloud Certified - Professional Cloud DevOps Engineer Exam certification verifies that the candidate possesses the necessary skills to design, implement, and manage DevOps processes using GCP tools and services.
NEW QUESTION # 78
You have a CI/CD pipeline that uses Cloud Build to build new Docker images and push them to Docker Hub.
You use Git for code versioning. After making a change in the Cloud Build YAML configuration, you notice that no new artifacts are being built by the pipeline. You need to resolve the issue following Site Reliability Engineering practices. What should you do?
- A. Change the CI pipeline to push the artifacts is Container Registry instead of Docker Hub.
- B. Upload the configuration YAML file to Cloud Storage and use Error Reporting to identify and fix the issue.
- C. Disable the CI pipeline and revert to manually building and pushing the artifacts.
- D. Run a Git compare between the previous and current Cloud Build Configuration files to find and fix the bug.
Answer: A
NEW QUESTION # 79
Your development team has created a new version of their service's API. You need to deploy the new versions of the API with the least disruption to third-party developers and end users of third-party installed applications.
What should you do?
- A. Introduce the new version of the API.
Announce deprecation of the old version of the API.
Deprecate the old version of the API.
Contact remaining users of the old API.
Provide best effort support to users of the old API.
Turn down the old version of the API. - B. Announce deprecation of the old version of the API.
Introduce the new version of the API.
Contact remaining users on the old API.
Deprecate the old version of the API.
Turn down the old version of the API.
Provide best effort support to users of the old API. - C. Announce deprecation of the old version of the API.
Contact remaining users on the old API.
Introduce the new version of the API.
Deprecate the old version of the API.
Provide best effort support to users of the old API.
Turn down the old version of the API. - D. Introduce the new version of the API.
Contact remaining users of the old API.
Announce deprecation of the old version of the API.
Deprecate the old version of the API.
Turn down the old version of the API.
Provide best effort support to users of the old API.
Answer: A
NEW QUESTION # 80
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_dataset You 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 an aggregated Iog sink in the Dev and Prod folders
- C. Create a log sink in each project
- D. Create a single aggregated log sink at the organization level.
Answer: B
Explanation:
The best option for minimizing the number of log sinks created and ensuring that the log sinks apply to future projects is to create an aggregated log sink in the Dev and Prod folders. An aggregated log sink is a log sink that collects logs from multiple sources, such as projects, folders, or organizations. By creating an aggregated log sink in each folder, you can export log entries for development projects to dev_dataset and log entries for production projects to prod_dataset. You can also use filters to specify which logs you want to export. Additionally, by creating an aggregated log sink at the folder level, you can ensure that the log sink applies to future projects that are created under that folder.
NEW QUESTION # 81
You support a high-traffic web application that runs on Google Cloud Platform (GCP). You need measure application reliability from a user perspective without making any engineering changes to it. What should you do? (Choose two.)
- A. Review current application metrics and add new ones as needed.
- B. Analyze the web proxy logs only and capture response time of each request.
- C. Use current and historic Request Logs to trace customer interaction with the application.
- D. Create new synthetic clients to simulate a user journey using the application.
- E. Modify the code to capture additional information for user interaction.
Answer: D,E
NEW QUESTION # 82
You are on-call for an infrastructure service that has a large number of dependent systems. You receive an alert indicating that the service is failing to serve most of its requests and all of its dependent systems with hundreds of thousands of users are affected. As part of your Site Reliability Engineering (SRE) incident management protocol, you declare yourself Incident Commander (IC) and pull in two experienced people from your team as Operations Lead (OLJ and Communications Lead (CL). What should you do next?
- A. Start a postmortem, add incident information, circulate the draft internally, and ask internal stakeholders for input.
- B. Establish a communication channel where incident responders and leads can communicate with each other.
- C. Contact the affected service owners and update them on the status of the incident.
- D. Look for ways to mitigate user impact and deploy the mitigations to production.
Answer: D
NEW QUESTION # 83
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 cross-service federation for centralized data access
- B. Configure Prometheus hierarchical federation for centralized data access
- C. Configure Google Cloud Managed Service for Prometheus
- D. Configure workload metrics within Cloud Operations for GKE
Answer: C
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 # 84
You need to create a Cloud Monitoring SLO for a service that will be published soon. You want to verify that requests to the service will be addressed in fewer than 300 ms at least 90% Of the time per calendar month. You need to identify the metric and evaluation method to use. What should you do?
- A. Select an availability metric for a window-based method Of evaluation.
- B. Select a latency metric for a window-based method of evaluation.
- C. Select a latency metric for a request-based method of evaluation.
- D. Select an availability metric for a request-based method of evaluation.
Answer: C
Explanation:
The correct answer is
A, Select a latency metric for a request-based method of evaluation.
A latency metric measures how responsive your service is to users. For example, you can use the cloud.googleapis.com/http/server/response_latencies metric to measure the latency of HTTP requests to your service1. A request-based method of evaluation counts the number of successful requests that meet a certain criterion, such as being below a latency threshold, and compares it to the number of all requests. For example, you can define an SLI as the ratio of requests with latency below 300 ms to all requests2. A request-based method of evaluation is suitable for measuring performance over time, such as per calendar month. You can set an SLO for the SLI to be at least 90%, which means that you expect 90% of the requests to have latency below 300 ms in a month3.
Reference:
Creating an SLO | Operations Suite | Google Cloud, Choosing a metric, Latency metric. Concepts in service monitoring | Operations Suite | Google Cloud, Service-level indicators, Request-based SLIs. Learn how to set SLOs - SRE tips | Google Cloud Blog, Setting SLOs.
NEW QUESTION # 85
You are running an experiment to see whether your users like a new feature of a web application. Shortly after deploying the feature as a canary release, you receive a spike in the number of 500 errors sent to users, and your monitoring reports show increased latency. You want to quickly minimize the negative impact on users. What should you do first?
- A. Start monitoring latency, traffic, errors, and saturation.
- B. Roll back the experimental canary release.
- C. Trace the origin of 500 errors and the root cause of increased latency.
- D. Record data for the postmortem document of the incident.
Answer: A
NEW QUESTION # 86
You are monitoring a service that uses n2-standard-2 Compute Engine instances that serve large files. Users have reported that downloads are slow. Your Cloud Monitoring dashboard shows that your VMS are running at peak network throughput. You want to improve the network throughput performance. What should you do?
- A. Deploy a Cloud NAT gateway and attach the gateway to the subnet of the VMS.
- B. Change the machine type for your VMS to n2-standard-8.
- C. Add additional network interface controllers (NICs) to your VMS.
- D. Deploy the Ops Agent to export additional monitoring metrics.
Answer: B
Explanation:
Explanation
The correct answer is C. Change the machine type for your VMs to n2-standard-8.
According to the Google Cloud documentation, the network throughput performance of a Compute Engine VM depends on its machine type1. The n2-standard-2 machine type has a maximum egress bandwidth of 4 Gbps, which can be a bottleneck for serving large files. By changing the machine type to n2-standard-8, you can increase the maximum egress bandwidth to 16 Gbps, which can improve the network throughput performance and reduce the download time for users. You also need to enable per VM Tier_1 networking performance, which is a feature that allows VMs to achieve higher network performance than the default settings2.
The other options are incorrect because they do not improve the network throughput performance of your VMs. Option A is incorrect because Cloud NAT is a service that allows private IP addresses to access the internet, but it does not increase the network bandwidth or speed3. Option B is incorrect because adding additional network interfaces (NICs) or IP addresses per NIC does not increase ingress or egress bandwidth for a VM1. Option D is incorrect because deploying the Ops Agent can help you monitor and troubleshoot your VMs, but it does not affect the network throughput performance4.
NEW QUESTION # 87
Your company is developing applications that are deployed on Google Kubernetes Engine (GKE). Each team manages a different application. You need to create the development and production environments for each team, while minimizing costs. Different teams should not be able to access other teams' environments. What should you do?
- A. Create one GCP Project per team. In each project, create a cluster for Development and one for Production. Grant the teams IAM access to their respective clusters.
- B. Create one GCP Project per team. In each project, create a cluster with a Kubernetes namespace for Development and one for Production. Grant the teams IAM access to their respective clusters.
- C. Create a Development and a Production GKE cluster in separate projects. In each cluster, create a Kubernetes namespace per team, and then configure Kubernetes Role-based access control (RBAC) so that each team can only access its own namespace.
- D. Create a Development and a Production GKE cluster in separate projects. In each cluster, create a Kubernetes namespace per team, and then configure Identity Aware Proxy so that each team can only access its own namespace.
Answer: C
Explanation:
Explanation
https://cloud.google.com/architecture/prep-kubernetes-engine-for-prod#roles_and_groups
NEW QUESTION # 88
Your applications performance in Google Cloud has degraded since the last release You suspect that downstream dependencies might be causing some requests to take longer to complete You need to investigate the issue with your application to determine the cause What should you do?
- A. Configure Cloud Trace in your application
- B. Configure Error Reporting in your application
- C. Configure Google Cloud Managed Service for Prometheus in your application
- D. Configure Cloud Profiler in your application
Answer: A
Explanation:
The best option for investigating the issue with your application's performance in Google Cloud is to configure Cloud Trace in your application. Cloud Trace is a service that allows you to collect and analyze latency data from your application. You can use Cloud Trace to trace requests across different components of your application, such as downstream dependencies, and identify where they take longer to complete. You can also use Cloud Trace to compare latency data across different versions of your application, and detect any performance degradation or improvement. By using Cloud Trace, you can diagnose and troubleshoot performance issues with your application in Google Cloud.
NEW QUESTION # 89
Your application runs on Google Cloud Platform (GCP). You need to implement Jenkins for deploying application releases to GCP. You want to streamline the release process, lower operational toil, and keep user data secure. What should you do?
- A. Implement Jenkins on local workstations.
- B. Implement Jenkins on Compute Engine virtual machines.
- C. Implement Jenkins on Google Cloud Functions.
- D. Implement Jenkins on Kubernetes on-premises
Answer: B
NEW QUESTION # 90
A third-party application needs to have a service account key to work properly When you try to export the key from your cloud project you receive an error "The organization policy constraint larn.disableServiceAccountKeyCreation is enforcedM You need to make the third-party application work while following Google-recommended security practices What should you do?
- A. Remove the iam.disableServiceAccountKeyCreation policy at the organization level, and create a key.
- B. Disable the service account key creation policy at the project's folder, and download the default key
- C. Enable the default service account key. and download the key
- D. Add a rule to set the iam.disableServiceAccountKeyCreation policy to off in your project and create a key.
Answer: D
Explanation:
Explanation
The best option for making the third-party application work while following Google-recommended security practices is to add a rule to set the iam.disableServiceAccountKeyCreation policy to off in your project and create a key. The iam.disableServiceAccountKeyCreation policy is an organization policy that controls whether service account keys can be created in a project or organization. By default, this policy is set to on, which means that service account keys cannot be created. However, you can override this policy at a lower level, such as a project, by adding a rule to set it to off. This way, you can create a service account key for your project without affecting other projects or organizations. You should also follow the best practices for managing service account keys, such as rotating them regularly, storing them securely, and deleting them when they are no longer needed.
NEW QUESTION # 91
You are building and deploying a microservice on Cloud Run for your organization Your service is used by many applications internally You are deploying a new release, and you need to test the new version extensively in the staging and production environments You must minimize user and developer impact. What should you do?
- A. Deploy the new version of the service to the staging environment Split the traffic, and allow 50% of traffic through to the latest version Test the latest version If the test passes, send all traffic to the latest version Repeat for the production environment
- B. Deploy a new environment with the green tag to use as the staging environment Deploy the new version of the service to the green environment and test the new version If the tests pass, send all traffic to the green environment and delete the existing staging environment Repeat for the production environment
- C. Deploy the new version of the service to the staging environment with a new-release tag without serving traffic Test the new-release version If the test passes; gradually roll out this tagged version Repeat for the production environment
- D. Deploy the new version of the service to the staging environment Split the traffic, and allow 1 % of traffic through to the latest version Test the latest version If the test passes gradually roll out the latest version to the staging and production environments
Answer: C
Explanation:
Explanation
The best option for deploying a new release of your microservice on Cloud Run and testing it extensively in the staging and production environments with minimal user and developer impact is to deploy the new version of the service to the staging environment with a new-release tag without serving traffic, test the new-release version, and if the test passes, gradually roll out this tagged version. A tag is a label that you can assign to a revision of your service on Cloud Run. You can use tags to create different versions of your service without affecting traffic. You can also use tags to gradually roll out traffic to a new version of your service by using traffic splitting. This way, you can test your new release extensively in both environments and minimize user and developer impact.
NEW QUESTION # 92
Your team is running microservices in Google Kubernetes Engine (GKE) You want to detect consumption of an error budget to protect customers and define release policies What should you do?
- A. Use the metrics from Anthos Service Mesh to measure the health of the microservices
- B. Create a SLO and configure uptime checks for your services Enable Alert Policies if the services do not pass
- C. Create a SLO Create an Alert Policy on select_slo_bum_rate
- D. Create SLIs from metrics Enable Alert Policies if the services do not pass
Answer: C
Explanation:
The best option for detecting consumption of an error budget to protect customers and define release policies is to create a service level objective (SLO) and create an alert policy on select_slo_burn_rate. A SLO is a target value or range of values for a service level indicator (SLI) that measures some aspect of the service quality, such as availability or latency. An error budget is the amount of time or number of errors that a service can tolerate while still meeting its SLO. A select_slo_burn_rate is a metric that indicates how fast the error budget is being consumed by the service. By creating an alert policy on select_slo_burn_rate, you can trigger notifications or actions when the error budget consumption exceeds a certain threshold. This way, you can balance change, velocity, and reliability of the service by adjusting the release policies based on the error budget status.
NEW QUESTION # 93
You manage an application that is writing logs to Stackdriver Logging. You need to give some team members the ability to export logs. What should you do?
- A. Create and grant a custom IAM role with the permissions logging.sinks.list and logging.sink.get.
- B. Create an Organizational Policy in Cloud IAM to allow only these members to create log exports.
- C. Configure Access Context Manager to allow only these members to export logs.
- D. Grant the team members the IAM role of logging.configWriter on Cloud IAM.
Answer: D
Explanation:
https://cloud.google.com/logging/docs/access-control
NEW QUESTION # 94
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. 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.
- B. 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.
- C. Grant relevant team members read access to all GCP production projects. Create Stackdriver workspaces inside each project.
- D. Grant relevant team members the Project Viewer IAM role on all GCP production projects. Create Slackdriver workspaces inside each project.
Answer: B
NEW QUESTION # 95
Your organization wants to collect system logs that will be used to generate dashboards in Cloud Operations for their Google Cloud project. You need to configure all current and future Compute Engine instances to collect the system logs and you must ensure that the Ops Agent remains up to date. What should you do?
- A. Use the gcloud CLI to create an Agent Policy.
- B. Use the gcloud CLI to install the Ops Agent on each VM listed in the Cloud Asset Inventory
- C. Install the Ops Agent on the Compute Engine image by using a startup script
- D. Select all VMs with an Agent status of Not detected on the Cloud Operations VMs dashboard Then select Install agents
Answer: A
Explanation:
Explanation
The best option for configuring all current and future Compute Engine instances to collect system logs and ensure that the Ops Agent remains up to date is to use the gcloud CLI to create an Agent Policy. An Agent Policy is a resource that defines how Ops Agents are installed and configured on VM instances that match certain criteria, such as labels or zones. Ops Agents are software agents that collect metrics and logs from VM instances and send them to Cloud Operations products, such as Cloud Monitoring and Cloud Logging. By creating an Agent Policy, you can ensure that all current and future VM instances that match the policy criteria will have the Ops Agent installed and updated automatically. This way, you can collect system logs from all VM instances and use them to generate dashboards in Cloud Operations.
NEW QUESTION # 96
Your company follows Site Reliability Engineering practices. You are the Incident Commander for a new.
customer-impacting incident. You need to immediately assign two incident management roles to assist you in an effective incident response. What roles should you assign?
Choose 2 answers
- A. Operations Lead
- B. Customer Impact Assessor
- C. Communications Lead
- D. Engineering Lead
- E. External Customer Communications Lead
Answer: A,C
Explanation:
Explanation
https://sre.google/workbook/incident-response/
"The main roles in incident response are the Incident Commander (IC), Communications Lead (CL), and Operations or Ops Lead (OL)." The Operations Lead is responsible for managing the operational aspects of the incident, such as deploying fixes, rolling back changes, or restoring backups. The External Customer Communications Lead is not a standard role in incident response, but it could be delegated by the Communications Lead if needed.
NEW QUESTION # 97
You support an application running on App Engine. The application is used globally and accessed from various device types. You want to know the number of connections. You are using Stackdriver Monitoring for App Engine. What metric should you use?
- A. flex/instance/connections/current
- B. tcp_ssl_proxy/new_connections
- C. flex/connections/current
- D. tcp_ssl_proxy/open_connections
Answer: C
Explanation:
Explanation
https://cloud.google.com/monitoring/api/metrics_gcp#gcp-appengine
NEW QUESTION # 98
You are analyzing Java applications in production. All applications have Cloud Profiler and Cloud Trace installed and configured by default. You want to determine which applications need performance tuning. What should you do?
Choose 2 answers
- A. Examine the wall-clock time and the CPU time Of the application. If the difference is substantial, increase the CPU resource allocation.
- B. 17 Examine the wall-clock time and the CPU time of the application. If the difference is substantial, increase the local disk storage allocation.
- C. Examine the heap usage Of the application. If the usage is low, mark the application for optimization.
- D. O Examine the latency time, the wall-clock time, and the CPU time of the application. If the latency time is slowly burning down the error budget, and the difference between wall-clock time and CPU time is minimal, mark the application for optimization.
- E. Examine the wall-clock time and the CPU time of the application. If the difference is substantial, increase the memory resource allocation.
Answer: A,D
Explanation:
The correct answers are A and D.
Examine the wall-clock time and the CPU time of the application. If the difference is substantial, increase the CPU resource allocation. This is a good way to determine if the application is CPU-bound, meaning that it spends more time waiting for the CPU than performing actual computation. Increasing the CPU resource allocation can improve the performance of CPU-bound applications1.
Examine the latency time, the wall-clock time, and the CPU time of the application. If the latency time is slowly burning down the error budget, and the difference between wall-clock time and CPU time is minimal, mark the application for optimization. This is a good way to determine if the application is I/O-bound, meaning that it spends more time waiting for input/output operations than performing actual computation.
Increasing the CPU resource allocation will not help I/O-bound applications, and they may need optimization to reduce the number or duration of I/O operations2.
Answer B is incorrect because increasing the memory resource allocation will not help if the application is CPU-bound or I/O-bound. Memory allocation affects how much data the application can store and access in memory, but it does not affect how fast the application can process that data.
Answer C is incorrect because increasing the local disk storage allocation will not help if the application is CPU-bound or I/O-bound. Disk storage affects how much data the application can store and access on disk, but it does not affect how fast the application can process that data.
Answer E is incorrect because examining the heap usage of the application will not help to determine if the application needs performance tuning. Heap usage affects how much memory the application allocates for dynamic objects, but it does not affect how fast the application can process those objects. Moreover, low heap usage does not necessarily mean that the application is inefficient or unoptimized.
NEW QUESTION # 99
Your product is currently deployed in three Google Cloud Platform (GCP) zones with your users divided between the zones. You can fail over from one zone to another, but it causes a 10-minute service disruption for the affected users. You typically experience a database failure once per quarter and can detect it within five minutes. You are cataloging the reliability risks of a new real-time chat feature for your product. You catalog the following information for each risk:
* Mean Time to Detect (MUD} in minutes
* Mean Time to Repair (MTTR) in minutes
* Mean Time Between Failure (MTBF) in days
* User Impact Percentage
The chat feature requires a new database system that takes twice as long to successfully fail over between zones. You want to account for the risk of the new database failing in one zone. What would be the values for the risk of database failover with the new system?
- A. MTTD:5
MTTR: 20
MTBF: 90
Impact: 33% - B. MTTD:5
MTTR: 10
MTBF: 90
Impact 50% - C. MTTD:5
MTTR: 20
MTBF: 90
Impact: 50% - D. MTTD: 5
MTTR: 10
MTBF: 90
Impact: 33%
Answer: A
Explanation:
https://www.atlassian.com/incident-management/kpis/common-metrics
https://linkedin.github.io/school-of-sre/
NEW QUESTION # 100
Your organization recently adopted a container-based workflow for application development. Your team develops numerous applications that are deployed continuously through an automated build pipeline to a Kubernetes cluster in the production environment. The security auditor is concerned that developers or operators could circumvent automated testing and push code changes to production without approval. What should you do to enforce approvals?
- A. Configure the build system with protected branches that require pull request approval.
- B. Leverage Kubernetes Role-Based Access Control (RBAC) to restrict access to only approved users.
- C. Use an Admission Controller to verify that incoming requests originate from approved sources.
- D. Enable binary authorization inside the Kubernetes cluster and configure the build pipeline as an attestor.
Answer: A
NEW QUESTION # 101
......
Google Professional-Cloud-DevOps-Engineer certification is a valuable credential for professionals who want to demonstrate their expertise in GCP and DevOps practices. It can help professionals advance their careers by demonstrating their ability to design, develop, and manage GCP solutions using DevOps principles. Google Cloud Certified - Professional Cloud DevOps Engineer Exam certification also demonstrates to potential employers that the candidate has the skills and knowledge needed to manage GCP resources efficiently and effectively.
Professional-Cloud-DevOps-Engineer Certification Study Guide Pass Professional-Cloud-DevOps-Engineer Fast: https://www.pass4sures.top/Cloud-DevOps-Engineer/Professional-Cloud-DevOps-Engineer-testking-braindumps.html
Free Professional-Cloud-DevOps-Engineer Certification Sample Questions with Online Practice Test: https://drive.google.com/open?id=1HxEaxbQ7zQ0aRrl1k4uXSSQe9bscxXLx