Professional-Cloud-DevOps-Engineer Free Study Guide! with New Update 166 Exam Questions
Get up-to-date Real Exam Questions for Professional-Cloud-DevOps-Engineer UPDATED [2024]
Google Professional-Cloud-DevOps-Engineer exam is a certification offered by Google Cloud that focuses on validating the skills and knowledge of professionals in the field of cloud-based DevOps. Google Cloud Certified - Professional Cloud DevOps Engineer Exam certification is intended for individuals who have a strong understanding of cloud-based infrastructure, continuous integration and delivery, automation, and collaboration. Professional-Cloud-DevOps-Engineer exam is designed to assess a candidate’s ability to design, implement, and manage DevOps workflows on Google Cloud.
NEW QUESTION # 55
You are working with a government agency that requires you to archive application logs for seven years. You need to configure Stackdriver to export and store the logs while minimizing costs of storage. What should you do?
- A. Develop an App Engine application that pulls the logs from Stackdriver and saves them in BigQuery.
- B. Create a Cloud Storage bucket and develop your application to send logs directly to the bucket.
- C. Create a sink in Stackdriver, name it, create a bucket on Cloud Storage for storing archived logs, and then select the bucket as the log export destination.
- D. Create an export in Stackdriver and configure Cloud Pub/Sub to store logs in permanent storage for seven years.
Answer: C
Explanation:
Explanation
https://cloud.google.com/logging/docs/routing/overview
NEW QUESTION # 56
You support a large service with a well-defined Service Level Objective (SLO). The development team deploys new releases of the service multiple times a week. If a major incident causes the service to miss its SLO, you want the development team to shift its focus from working on features to improving service reliability. What should you do before a major incident occurs?
- A. Negotiate with the product team to always prioritize service reliability over releasing new features.
- B. Add a plugin to your Jenkins pipeline that prevents new releases whenever your service is out of SLO.
- C. Negotiate with the development team to reduce the release frequency to no more than once a week.
- D. Develop an appropriate error budget policy in cooperation with all service stakeholders.
Answer: D
Explanation:
Explanation
Reason : Incident has not occurred yet, even when development team is already pushing new features multiple times a week. The option A says, to define an error budget "policy", not to define error budget(It is already present). Just simple means to bring in all stakeholders, and decide how to consume the error budget effectively that could bring balance between feature deployment and reliability.
The goals of this policy are to: -- Protect customers from repeated SLO misses -- Provide an incentive to balance reliability with other features https://sre.google/workbook/error-budget-policy/
NEW QUESTION # 57
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 SLIs from metrics Enable Alert Policies if the services do not pass
- C. Create a SLO Create an Alert Policy on select_slo_bum_rate
- D. Create a SLO and configure uptime checks for your services 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 # 58
You have deployed a fleet Of Compute Engine instances in Google Cloud. You need to ensure that monitoring metrics and logs for the instances are visible in Cloud Logging and Cloud Monitoring by your company's operations and cyber security teams. You need to grant the required roles for the Compute Engine service account by using Identity and Access Management (IAM) while following the principle of least privilege. What should you do?
- A. Grant the Logging. admin and monitoring . editor roles to the Compute Engine service accounts.
- B. Grant the logging. logwriter and monitoring. editor roles to the Compute Engine service accounts.
- C. Grant the logging.editor and monitoring.metricwriter roles to the Compute Engine service accounts.
- D. Grant the logging. logWriter and monitoring. metricWriter roles to the Compute Engine service accounts.
Answer: C
Explanation:
The correct answer is D. Grant the logging.logWriter and monitoring.metricWriter roles to the Compute Engine service accounts.
According to the Google Cloud documentation, the Compute Engine service account is a Google-managed service account that is automatically created when you enable the Compute Engine API1. This service account is used by default to run your Compute Engine instances and access other Google Cloud services on your behalf1. To ensure that monitoring metrics and logs for the instances are visible in Cloud Logging and Cloud Monitoring, you need to grant the following IAM roles to the Compute Engine service account23:
The logging.logWriter role allows the service account to write log entries to Cloud Logging4.
The monitoring.metricWriter role allows the service account to write custom metrics to Cloud Monitoring5.
These roles grant the minimum permissions that are needed for logging and monitoring, following the principle of least privilege. The other roles are either unnecessary or too broad for this purpose. For example, the logging.editor role grants permissions to create and update logs, log sinks, and log exclusions, which are not required for writing log entries6. The logging.admin role grants permissions to delete logs, log sinks, and log exclusions, which are not required for writing log entries and may pose a security risk if misused. The monitoring.editor role grants permissions to create and update alerting policies, uptime checks, notification channels, dashboards, and groups, which are not required for writing custom metrics.
Reference:
Service accounts, Service accounts. Setting up Stackdriver Logging for Compute Engine, Setting up Stackdriver Logging for Compute Engine. Setting up Stackdriver Monitoring for Compute Engine, Setting up Stackdriver Monitoring for Compute Engine. Predefined roles, Predefined roles. Predefined roles, Predefined roles. Predefined roles, Predefined roles. [Predefined roles], Predefined roles. [Predefined roles], Predefined roles.
NEW QUESTION # 59
You are currently planning how to display Cloud Monitoring metrics for your organization's Google Cloud projects. Your organization has three folders and six projects:
You want to configure Cloud Monitoring dashboards lo only display metrics from the projects within one folder You need to ensure that the dashboards do not display metrics from projects in the other folders You want to follow Google-recommended practices What should you do?
- A. Create new scoping projects for each folder
- B. Use the current app-one-prod project as the scoping project
- C. Use the current app-one-dev, app-one-staging and app-one-prod projects as the scoping project for each folder
- D. Create a single new scoping project
Answer: A
Explanation:
Explanation
The best option for configuring Cloud Monitoring dashboards to only display metrics from the projects within one folder is to create new scoping projects for each folder. A scoping project is a project that defines which resources are monitored by Cloud Monitoring. You can create new scoping projects for each folder by using the gcloud monitoring register-project command. This way, you can associate each scoping project with a folder and only monitor the resources within that folder. You can then configure Cloud Monitoring dashboards to use the scoping projects as data sources and only display metrics from the projects within one folder.
NEW QUESTION # 60
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 n1-highcpu-96 machine type in the configuration of the MIG.
- C. Monitor results of Stackdriver Trace to determine the required amount of resources.
- D. Validate that the resource requirements are within the available quota limits of each region.
Answer: B
NEW QUESTION # 61
You are ready to deploy a new feature of a web-based application to production. You want to use Google Kubernetes Engine (GKE) to perform a phased rollout to half of the web server pods.
What should you do?
- A. Use a replica set in the deployment specification.
- B. Use a stateful set with parallel pod management policy.
- C. Use Node taints with NoExecute.
- D. Use a partitioned rolling update.
Answer: D
Explanation:
https://medium.com/velotio-perspectives/exploring-upgrade-strategies-for-stateful-sets-in-kubernetes-c02b8286f251
NEW QUESTION # 62
You are currently planning how to display Cloud Monitoring metrics for your organization's Google Cloud projects. Your organization has three folders and six projects:
You want to configure Cloud Monitoring dashboards lo only display metrics from the projects within one folder You need to ensure that the dashboards do not display metrics from projects in the other folders You want to follow Google-recommended practices What should you do?
- A. Create new scoping projects for each folder
- B. Use the current app-one-prod project as the scoping project
- C. Use the current app-one-dev, app-one-staging and app-one-prod projects as the scoping project for each folder
- D. Create a single new scoping project
Answer: A
Explanation:
The best option for configuring Cloud Monitoring dashboards to only display metrics from the projects within one folder is to create new scoping projects for each folder. A scoping project is a project that defines which resources are monitored by Cloud Monitoring. You can create new scoping projects for each folder by using the gcloud monitoring register-project command. This way, you can associate each scoping project with a folder and only monitor the resources within that folder. You can then configure Cloud Monitoring dashboards to use the scoping projects as data sources and only display metrics from the projects within one folder.
NEW QUESTION # 63
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 Kubernetes on-premises
- B. Implement Jenkins on Compute Engine virtual machines.
- C. Implement Jenkins on Google Cloud Functions.
- D. Implement Jenkins on local workstations.
Answer: B
Explanation:
Explanation
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?
https://plugins.jenkins.io/google-compute-engine/
NEW QUESTION # 64
You are developing reusable infrastructure as code modules. Each module contains integration tests that launch the module in a test project. You are using GitHub for source control. You need to Continuously test your feature branch and ensure that all code is tested before changes are accepted. You need to implement a solution to automate the integration tests. What should you do?
- A. Use Cloud Build to run tests in a specific folder. Trigger Cloud Build for every GitHub pull request.
- B. Use Cloud Build to run the tests. Trigger all tests to run after a pull request is merged.
- C. Ask the pull request reviewers to run the integration tests before approving the code.
- D. Use a Jenkins server for Cl/CD pipelines. Periodically run all tests in the feature branch.
Answer: A
Explanation:
Cloud Build is a service that executes your builds on Google Cloud Platform infrastructure. Cloud Build can import source code from Google Cloud Storage, Cloud Source Repositories, GitHub, or Bitbucket, execute a build to your specifications, and produce artifacts such as Docker containers or Java archives1. Cloud Build can also run integration tests as part of your build steps2.
You can use Cloud Build to run tests in a specific folder by specifying the path to the folder in the dir field of your build step3. For example, if you have a folder named tests that contains your integration tests, you can use the following build step to run them:
steps:
- name: 'gcr.io/cloud-builders/go'
args: ['test', '-v']
dir: 'tests'
Copy
You can use Cloud Build to trigger builds for every GitHub pull request by using the Cloud Build GitHub app. The app allows you to automatically build on Git pushes and pull requests and view your build results on GitHub and Google Cloud console4. You can configure the app to run builds on specific branches, tags, or paths5. For example, if you want to run builds on pull requests that target the master branch, you can use the following trigger configuration:
includedFiles:
- '**'
name: 'pull-request-trigger'
github:
name: 'my-repo'
owner: 'my-org'
pullRequest:
branch: '^master$'
Using Cloud Build to run tests in a specific folder and trigger builds for every GitHub pull request is a good way to continuously test your feature branch and ensure that all code is tested before changes are accepted. This way, you can catch any errors or bugs early and prevent them from affecting the main branch.
Using a Jenkins server for CI/CD pipelines is not a bad option, but it would require more setup and maintenance than using Cloud Build, which is fully managed by Google Cloud. Periodically running all tests in the feature branch is not as efficient as running tests for every pull request, as it may delay the feedback loop and increase the risk of conflicts or failures.
Using Cloud Build to run the tests after a pull request is merged is not a good practice, as it may introduce errors or bugs into the main branch that could have been prevented by testing before merging.
Asking the pull request reviewers to run the integration tests before approving the code is not a reliable way of ensuring code quality, as it depends on human intervention and may be prone to errors or oversights.
Reference:
1: Overview | Cloud Build Documentation | Google Cloud
2: Running integration tests | Cloud Build Documentation | Google Cloud
3: Build configuration overview | Cloud Build Documentation | Google Cloud
4: Building repositories from GitHub | Cloud Build Documentation | Google Cloud
5: Creating GitHub app triggers | Cloud Build Documentation | Google Cloud
NEW QUESTION # 65
Your company has a Google Cloud resource hierarchy with folders for production test and development Your cyber security team needs to review your company's Google Cloud security posture to accelerate security issue identification and resolution You need to centralize the logs generated by Google Cloud services from all projects only inside your production folder to allow for alerting and near-real time analysis. What should you do?
- A. Create a central Cloud Monitoring workspace and attach all related projects
- B. Create an aggregated log sink associated with the production folder that uses a Cloud Logging bucket as the destination
- C. Enable the Workflows API and route all the logs to Cloud Logging
- D. Create an aggregated log sink associated with the production folder that uses a Pub Sub topic as the destination
Answer: B
Explanation:
The best option for centralizing the logs generated by Google Cloud services from all projects only inside your production folder is to create an aggregated log sink associated with the production folder that uses a Cloud Logging bucket as the destination. An aggregated log sink is a log sink that collects logs from multiple sources, such as projects, folders, or organizations. A Cloud Logging bucket is a storage location for logs that can be used as a destination for log sinks. By creating an aggregated log sink with a Cloud Logging bucket, you can collect and store all the logs from the production folder in one place and allow for alerting and near-real time analysis using Cloud Monitoring and Cloud Operations.
NEW QUESTION # 66
You support an e-commerce application that runs on a large Google Kubernetes Engine (GKE) cluster deployed on-premises and on Google Cloud Platform. The application consists of microservices that run in containers. You want to identify containers that are using the most CPU and memory. What should you do?
- A. Use the Stackdriver Monitoring API to create custom metrics, and then organize your containers using groups.
- B. Use Stackdriver Logging to export application logs to BigOuery. aggregate logs per container, and then analyze CPU and memory consumption.
- C. Use Prometheus to collect and aggregate logs per container, and then analyze the results in Grafana.
- D. Use Stackdriver Kubernetes Engine Monitoring.
Answer: D
Explanation:
Explanation
https://cloud.google.com/anthos/clusters/docs/on-prem/1.7/concepts/logging-and-monitoring
NEW QUESTION # 67
Your organization wants to increase the availability target of an application from 99 9% to 99 99% for an investment of $2 000 The application's current revenue is S1,000,000 You need to determine whether the increase in availability is worth the investment for a single year of usage What should you do?
- A. Calculate the value of improved availability to be $9,000. and determine that the increase in availability is worth the investment
- B. Calculate the value of improved availability to be $1 000 and determine that the increase in availability is worth the investment
- C. Calculate the value of improved availability to be $900, and determine that the increase in availability is not worth the investment
- D. Calculate the value of improved availability to be $1 000 and determine that the increase in availability is not worth the investment
Answer: C
Explanation:
Explanation
The best option for determining whether the increase in availability is worth the investment for a single year of usage is to calculate the value of improved availability to be $900, and determine that the increase in availability is not worth the investment. To calculate the value of improved availability, we can use the following formula:
Value of improved availability = Revenue * (New availability - Current availability) Plugging in the given numbers, we get:
Value of improved availability = $1,000,000 * (0.9999 - 0.999) = $900
Since the value of improved availability is less than the investment of $2,000, we can conclude that the increase in availability is not worth the investment.
NEW QUESTION # 68
You are developing the deployment and testing strategies for your CI/CD pipeline in Google Cloud You must be able to
* Reduce the complexity of release deployments and minimize the duration of deployment rollbacks
* Test real production traffic with a gradual increase in the number of affected users You want to select a deployment and testing strategy that meets your requirements What should you do?
- A. Recreate deployment and canary testing
- B. Blue/green deployment and canary testing
- C. Rolling update deployment and shadow testing
- D. Rolling update deployment and A/B testing
Answer: B
Explanation:
Explanation
The best option for selecting a deployment and testing strategy that meets your requirements is to use blue/green deployment and canary testing. A blue/green deployment is a deployment strategy that involves creating two identical environments, one running the current version of the application (blue) and one running the new version of the application (green). The traffic is switched from blue to green after testing the new version, and if any issues are discovered, the traffic can be switched back to blue instantly. This way, you can reduce the complexity of release deployments and minimize the duration of deployment rollbacks. A canary testing is a testing strategy that involves releasing a new version of an application to a subset of users or servers and monitoring its performance and reliability. This way, you can test real production traffic with a gradual increase in the number of affected users.
NEW QUESTION # 69
Your application services run in Google Kubernetes Engine (GKE). You want to make sure that only images from your centrally-managed Google Container Registry (GCR) image registry in the altostrat-images project can be deployed to the cluster while minimizing development time. What should you do?
- A. Use a Binary Authorization policy that includes the whitelist name pattern gcr.io/attostrat-images/.
- B. Add logic to the deployment pipeline to check that all manifests contain only images from gcr.io/altostrat-images.
- C. Add a tag to each image in gcr.io/altostrat-images and check that this tag is present when the image is deployed.
- D. Create a custom builder for Cloud Build that will only push images to gcr.io/altostrat-images.
Answer: C
NEW QUESTION # 70
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.
* Rename the folder structure with a predefined naming convention that increments the version. - B. 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. - 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 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. - D. 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.
Answer: B
NEW QUESTION # 71
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. Leverage Kubernetes Role-Based Access Control (RBAC) to restrict access to only approved users.
- B. Configure the build system with protected branches that require pull request approval.
- 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: D
Explanation:
Explanation
The keywords here is "developers or operators". Option A the operators could push images to production without approval (operators could touch the cluster directly and the cluster cannot do any action against them).
Rest same as francisco_guerra.
NEW QUESTION # 72
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. Review the Stackdriver monitoring.
- B. Upsize the virtual machines running the login services.
- C. Deploy a new release to see whether it fixes the problem.
- D. Roll back the recent release.
Answer: B
Explanation:
Rollback to previous stable version. Then you need to find what is causing the issue.
NEW QUESTION # 73
You support a multi-region web service running on Google Kubernetes Engine (GKE) behind a Global HTTP'S Cloud Load Balancer (CLB). For legacy reasons, user requests first go through a third-party Content Delivery Network (CDN). which then routes traffic to the CLB. You have already implemented an availability Service Level Indicator (SLI) at the CLB level. However, you want to increase coverage in case of a potential load balancer misconfiguration. CDN failure, or other global networking catastrophe. Where should you measure this new SLI?
Choose 2 answers
- A. Your application servers' logs
- B. Instrumentation coded directly in the client
- C. Metrics exported from the application servers
- D. GKE health checks for your application servers
- E. A synthetic client that periodically sends simulated user requests
Answer: B,E
NEW QUESTION # 74
Your company follows Site Reliability Engineering principles. You are writing a postmortem for an incident, triggered by a software change, that severely affected users. You want to prevent severe incidents from happening in the future. What should you do?
- A. Follow up with the employees who reviewed the changes and prescribe practices they should follow in the future.
- B. Ensure that test cases that catch errors of this type are run successfully before new software releases.
- C. Design a policy that will require on-call teams to immediately call engineers and management to discuss a plan of action if an incident occurs.
- D. Identify engineers responsible for the incident and escalate to their senior management.
Answer: B
Explanation:
Explanation
The best way to prevent severe incidents from happening in the future is to ensure that test cases that catch errors of this type are run successfully before new software releases. This is aligned with the Site Reliability Engineering principle of testing for reliability.
NEW QUESTION # 75
You are designing a system with three different environments: development, quality assurance (QA), and production.
Each environment will be deployed with Terraform and has a Google Kubemetes Engine (GKE) cluster created so that application teams can deploy their applications. Anthos Config Management will be used and templated to deploy infrastructure level resources in each GKE cluster. All users (for example, infrastructure operators and application owners) will use GitOps. How should you structure your source control repositories for both Infrastructure as Code (laC) and application code?
- A. Cloud Infrastructure (Terraform) repository is shared: different directories are different environments GKE Infrastructure (Anthos Config Management Kustomize manifests) repositories are separated:
different branches are different environments
Application (app source code) repositories are separated: different branches are different features - B. Cloud Infrastructure (Terraform) repository is shared: different directories are different environments GKE Infrastructure (Anthos Config Management Kustomize manifests) repository is shared: different overlay directories are different environments Application (app source code) repositories are separated: different branches are different features
- C. Cloud Infrastructure (Terraform) repositories are separated: different branches are different environments GKE Infrastructure (Anthos Config Management Kustomize manifests) repositories are separated:
different overlay directories are different environments
Application (app source code) repositories are separated: different branches are different features - D. Cloud Infrastructure (Terraform) repository is shared: different branches are different environments GKE Infrastructure (Anthos Config Management Kustomize manifests) repository is shared: different overlay directories are different environments Application (app source code) repository is shared: different directories are different features
Answer: A
Explanation:
The correct answer is B, Cloud Infrastructure (Terraform) repository is shared: different directories are different environments. GKE Infrastructure (Anthos Config Management Kustomize manifests) repositories are separated: different branches are different environments. Application (app source code) repositories are separated: different branches are different features.
This answer follows the best practices for using Terraform and Anthos Config Management with GitOps, as described in the following sources:
For Terraform, it is recommended to use a single repository for all environments, and use directories to separate them. This way, you can reuse the same Terraform modules and configurations across environments, and avoid code duplication and drift. You can also use Terraform workspaces to isolate the state files for each environment12.
For Anthos Config Management, it is recommended to use separate repositories for each environment, and use branches to separate the clusters within each environment. This way, you can enforce different policies and configurations for each environment, and use pull requests to promote changes across environments. You can also use Kustomize to create overlays for each cluster that apply specific patches or customizations34.
For application code, it is recommended to use separate repositories for each application, and use branches to separate the features or bug fixes for each application. This way, you can isolate the development and testing of each application, and use pull requests to merge changes into the main branch. You can also use tags or labels to trigger deployments to different environments5 .
Reference:
1: Best practices for using Terraform | Google Cloud
2: Terraform Recommended Practices - Part 1 | Terraform - HashiCorp Learn
3: Deploy Anthos on GKE with Terraform part 1: GitOps with Config Sync | Google Cloud Blog
4: Using Kustomize with Anthos Config Management | Anthos Config Management Documentation | Google Cloud
5: Deploy Anthos on GKE with Terraform part 3: Continuous Delivery with Cloud Build | Google Cloud Blog
6: GitOps-style continuous delivery with Cloud Build | Cloud Build Documentation | Google Cloud
NEW QUESTION # 76
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. Look for ways to mitigate user impact and deploy the mitigations to production.
- C. Establish a communication channel where incident responders and leads can communicate with each other.
- D. Contact the affected service owners and update them on the status of the incident.
Answer: B
Explanation:
Explanation
https://sre.google/sre-book/managing-incidents/
NEW QUESTION # 77
You recently migrated an ecommerce application to Google Cloud. You now need to prepare the application for the upcoming peak traffic season. You want to follow Google-recommended practices. What should you do first to prepare for the busy season?
- A. Create a Terraform configuration for the application's underlying infrastructure to quickly deploy to additional regions.
- B. Pre-provision the additional compute power that was used last season, and expect growth.
- C. Migrate the application to Cloud Run, and use autoscaling.
- D. Load test the application to profile its performance for scaling.
Answer: D
Explanation:
Explanation
The first thing you should do to prepare your ecommerce application for the upcoming peak traffic season is to load test the application to profile its performance for scaling. Load testing is a process of simulating high traffic or user demand on your application and measuring how it responds. Load testing can help you identify any bottlenecks, errors, or performance issues that might affect your application during the busy season1. Load testing can also help you determine the optimal scaling strategy for your application, such as horizontal scaling (adding more instances) or vertical scaling (adding more resources to each instance)2.
There are different tools and methods for load testing your ecommerce application on Google Cloud, depending on the type and complexity of your application. For example, you can use Cloud Load Balancing to distribute traffic across multiple instances of your application, and use Cloud Monitoring to measure the latency, throughput, and error rate of your application3. You can also use Cloud Functions or Cloud Run to create serverless load generators that can simulate user requests and send them to your application4.
Alternatively, you can use third-party tools such as Apache JMeter or Locust to create and run load tests on your application.
By load testing your ecommerce application before the peak traffic season, you can ensure that your application is ready to handle the expected load and provide a good user experience. You can also use the results of your load tests to plan and implement other steps to prepare your application for the busy season, such as migrating to a more scalable platform, creating a Terraform configuration for deploying to additional regions, or pre-provisioning additional compute power.
References:
1: Load Testing 101: How To Test Website Performance | BlazeMeter
2: Scaling applications | Google Cloud
3: Load testing using Google Cloud | Solutions | Google Cloud
4: Serverless load testing using Cloud Functions | Solutions | Google Cloud
NEW QUESTION # 78
......
Pass Google Professional-Cloud-DevOps-Engineer Exam in First Attempt Guaranteed: https://www.actualtorrent.com/Professional-Cloud-DevOps-Engineer-questions-answers.html
Pass Professional-Cloud-DevOps-Engineer Exam Latest Practice Questions: https://drive.google.com/open?id=1N5OGqtguO4INIZGJ85Va3wDWFAyyy_m-