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To be eligible for the Google Professional-Cloud-DevOps-Engineer certification exam, candidates should have at least three years of experience in software development, infrastructure management, and operations. They should also have a solid understanding of cloud technologies and DevOps practices, as well as experience in designing and implementing cloud-based solutions.
Google Professional-Cloud-DevOps-Engineer certification is highly valued in the industry and is recognized as a mark of excellence in the field of cloud-based DevOps. Google Cloud Certified - Professional Cloud DevOps Engineer Exam certification is ideal for professionals who are looking to advance their careers in DevOps and cloud computing, and who are interested in working with Google Cloud technologies. By earning this certification, professionals can demonstrate their ability to design, implement, and manage DevOps workflows on Google Cloud, and can enhance their credibility with employers and clients alike.
Google Professional-Cloud-DevOps-Engineer (Google Cloud Certified - Professional Cloud DevOps Engineer) Certification Exam is a highly sought-after certification in the technology industry. Google Cloud Certified - Professional Cloud DevOps Engineer Exam certification is aimed at professionals who are involved in the development, deployment, and management of cloud-based applications, services, and infrastructure. Professional-Cloud-DevOps-Engineer exam is designed to test the candidate's knowledge and skills in various areas such as cloud architecture, automation, security, compliance, and monitoring.
NEW QUESTION # 24
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. Enable binary authorization inside the Kubernetes cluster and configure the build pipeline as an attestor.
- B. Configure the build system with protected branches that require pull request approval.
- C. Leverage Kubernetes Role-Based Access Control (RBAC) to restrict access to only approved users.
- D. Use an Admission Controller to verify that incoming requests originate from approved sources.
Answer: A
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 # 25
You need to build a CI/CD pipeline for a containerized application in Google Cloud Your development team uses a central Git repository for trunk-based development You want to run all your tests in the pipeline for any new versions of the application to improve the quality What should you do?
- A. 1. Trigger Cloud Build to build the application container and run unit tests with the container
2. If unit tests are successful, deploy the application container to a testing environment, and run integration tests
3. If the integration tests are successful the pipeline deploys the application container to the production environment After that, run acceptance tests - B. 1. Install a Git hook to require developers to run unit tests before pushing the code to a central repository If all tests are successful build a container
2. Trigger Cloud Build to deploy the application container to a testing environment, and run integration tests and acceptance tests
3. If all tests are successful tag the code as production ready Trigger Cloud Build to build and deploy the application container to the production environment - C. 1. Trigger Cloud Build to run unit tests when the code is pushed If all unit tests are successful, build and push the application container to a central registry.
2. Trigger Cloud Build to deploy the container to a testing environment, and run integration tests and acceptance tests
3. If all tests are successful the pipeline deploys the application to the production environment and runs smoke tests - D. 1. Install a Git hook to require developers to run unit tests before pushing the code to a central repository
2. Trigger Cloud Build to build the application container Deploy the application container to a testing environment, and run integration tests
3. If the integration tests are successful deploy the application container to your production environment. and run acceptance tests
Answer: C
Explanation:
The best option for building a CI/CD pipeline for a containerized application in Google Cloud is to trigger Cloud Build to run unit tests when the code is pushed, if all unit tests are successful, build and push the application container to a central registry, trigger Cloud Build to deploy the container to a testing environment, and run integration tests and acceptance tests, and if all tests are successful, the pipeline deploys the application to the production environment and runs smoke tests. This option follows the best practices for CI/CD pipelines, such as running tests at different stages of the pipeline, using a central registry for storing and managing containers, deploying to different environments, and using Cloud Build as a unified tool for building, testing, and deploying.
NEW QUESTION # 26
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 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.
- B. 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.
- 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 network shared folder with child folders for each version release. Ensure that everyone works on different files.
Answer: C
Explanation:
https://www.terraform.io/docs/cloud/guides/recommended-practices/part3.3.html
NEW QUESTION # 27
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. Use the n1-highcpu-96 machine type in the configuration of the MIG.
- B. Validate that the resource requirements are within the available quota limits of each region.
- C. Deploy the service in one region and use a global load balancer to route traffic to this region.
- D. Monitor results of Stackdriver Trace to determine the required amount of resources.
Answer: C
NEW QUESTION # 28
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 the production environment. A recent security audit alerted your team that the code pushed to production could contain vulnerabilities and that the existing tooling around virtual machine (VM) vulnerabilities no longer applies to the containerized environment. You need to ensure the security and patch level of all code running through the pipeline. What should you do?
- A. Implement static code analysis tooling against the Docker files used to create the containers.
- B. Reconfigure the existing operating system vulnerability software to exist inside the container.
- C. Set up Container Analysis to scan and report Common Vulnerabilities and Exposures.
- D. Configure the containers in the build pipeline to always update themselves before release.
Answer: C
NEW QUESTION # 29
You are implementing a CI'CD pipeline for your application in your company s multi-cloud environment Your application is deployed by using custom Compute Engine images and the equivalent in other cloud providers You need to implement a solution that will enable you to build and deploy the images to your current environment and is adaptable to future changes Which solution stack should you use'?
- A. Google Kubernetes Engine with Google Cloud Deploy
- B. Cloud Build with Google Cloud Deploy
- C. Cloud Build with Packer
- D. Cloud Build with kpt
Answer: B
Explanation:
Explanation
Cloud Build is a fully managed continuous integration and continuous delivery (CI/CD) service that helps you automate your builds, tests, and deployments. Google Cloud Deploy is a service that automates the deployment of your applications to Google Kubernetes Engine (GKE).
Together, Cloud Build and Google Cloud Deploy can be used to build and deploy your application's custom Compute Engine images to your current environment and to other cloud providers in the future.
Here are the steps involved in using Cloud Build and Google Cloud Deploy to implement a CI/CD pipeline for your application:
Create a Cloud Build trigger that fires whenever a change is made to your application's code.
In the Cloud Build trigger, configure Cloud Build to build your application's Docker image.
Create a Google Cloud Deploy configuration file that specifies how to deploy your application's Docker image to GKE.
In Google Cloud Deploy, create a deployment that uses your configuration file.
Once you have created the Cloud Build trigger and Google Cloud Deploy configuration file, any changes made to your application's code will trigger Cloud Build to build a new Docker image. Google Cloud Deploy will then deploy the new Docker image to GKE.
This solution stack is adaptable to future changes because it uses a cloud-agnostic approach. Cloud Build can be used to build Docker images for any cloud provider, and Google Cloud Deploy can be used to deploy Docker images to any Kubernetes cluster.
The other solution stacks are not as adaptable to future changes. For example, solution stack A (Cloud Build with Packer) is limited to building Docker images for Compute Engine. Solution stack C (Google Kubernetes Engine with Google Cloud Deploy) is limited to deploying Docker images to GKE. Solution stack D (Cloud Build with kpt) is a newer solution that is not yet as mature as Cloud Build and Google Cloud Deploy.
Overall, the best solution stack for implementing a CI/CD pipeline for your application in a multi-cloud environment is Cloud Build with Google Cloud Deploy. This solution stack is fully managed, cloud-agnostic, and adaptable to future changes.
NEW QUESTION # 30
You support a user-facing web application When analyzing the application's error budget over the previous six months you notice that the application never consumed more than 5% of its error budget You hold a SLO review with business stakeholders and confirm that the SLO is set appropriately You want your application's reliability to more closely reflect its SLO What steps can you take to further that goal while balancing velocity, reliability, and business needs?
Choose 2 answers
- A. Add more serving capacity to all of your application's zones
- B. Have more frequent or potentially risky application releases
- C. Announce planned downtime to consume more error budget and ensure that users are not depending on a tighter SLO
- D. Tighten the SLO to match the application's observed reliability
- E. Implement and measure all other available SLIs for the application
Answer: B,D
Explanation:
Explanation
The best options for furthering your application's reliability goal while balancing velocity, reliability, and business needs are to have more frequent or potentially risky application releases and to tighten the SLO to match the application's observed reliability. Having more frequent or potentially risky application releases can help you increase the change velocity and deliver new features faster. However, this also increases the likelihood of consuming more error budget and reducing the reliability of your service. Therefore, you should monitor your error budget consumption and adjust your release policies accordingly. For example, you can freeze or slow down releases when the error budget is low, or accelerate releases when the error budget is high. Tightening the SLO to match the application's observed reliability can help you align your service quality with your users' expectations and business needs. However, this also means that you have less room for error and need to maintain a higher level of reliability. Therefore, you should ensure that your SLO is realistic and achievable, and that you have sufficient engineering resources and processes to meet it.
NEW QUESTION # 31
You recently deployed your application in Google Kubernetes Engine (GKE) and now need to release a new version of the application You need the ability to instantly roll back to the previous version of the application in case there are issues with the new version Which deployment model should you use?
- A. Perform a blue/green deployment and test your new application after the deployment is complete
- B. Perform a rolling deployment and test your new application after the deployment is complete
- C. Perform a testing, and test your application periodically after the deployment is complete
- D. Perform a canary deployment, and test your new application periodically after the new version is deployed
Answer: A
Explanation:
The best deployment model for releasing a new version of your application in GKE with the ability to instantly roll back to the previous version is to perform a blue/green deployment and test your new application after the deployment is complete. 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 minimize downtime and risk during deployment.
NEW QUESTION # 32
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. Create SLIs from metrics Enable Alert Policies if the services do not pass
- B. Use the metrics from Anthos Service Mesh to measure the health of the microservices
- 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 # 33
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. Use GCR digest versioning to match the image to the tag in source control.
- D. Supply the source control tag as a parameter within the image name.
Answer: D
Explanation:
https://cloud.google.com/container-registry/docs/pushing-and-pulling
NEW QUESTION # 34
You are creating a CI/CD pipeline in Cloud Build to build an application container image The application code is stored in GitHub Your company requires thai production image builds are only run against the main branch and that the change control team approves all pushes to the main branch You want the image build to be as automated as possible What should you do?
Choose 2 answers
- A. Create a trigger on the Cloud Build job Set the repository event setting to Push to a branch
- B. Create a trigger on the Cloud Build job Set the repository event setting to Pull request'
- C. Configure a branch protection rule for the main branch on the repository
- D. Enable the Approval option on the trigger
- E. Add the owners file to the Included files filter on the trigger
Answer: A,C
Explanation:
Explanation
The best options for creating a CI/CD pipeline in Cloud Build to build an application container image and ensuring that production image builds are only run against the main branch and that the change control team approves all pushes to the main branch are to create a trigger on the Cloud Build job, set the repository event setting to Push to a branch, and configure a branch protection rule for the main branch on the repository. A trigger is a resource that starts a build when an event occurs, such as a code change. By creating a trigger on the Cloud Build job and setting the repository event setting to Push to a branch, you can ensure that the image build is only run when code is pushed to a specific branch, such as the main branch. A branch protection rule is a rule that enforces certain policies on a branch, such as requiring reviews, status checks, or approvals before merging code. By configuring a branch protection rule for the main branch on the repository, you can ensure that the change control team approves all pushes to the main branch.
NEW QUESTION # 35
You need to define SLOs for a high-traffic web application. Customers are currently happy with the application performance and availability. Based on current measurement, the 90th percentile Of latency is 160 ms and the 95th percentile of latency is 300 ms over a 28-day window. What latency SLO should you publish?
- A. 90th percentile - 190 ms
95th percentile - 330 ms - B. 90th percentile - 300 ms
95th percentile - 450 ms - C. 90th percentile - 150 ms
95th percentile - 290 ms - D. 90th percentile - 160 ms
95th percentile - 300 ms
Answer: D
Explanation:
Explanation
a latency SLO is a service level objective that specifies a target level of responsiveness for a web application1. A latency SLO can be expressed as a percentile of latency over a time window, such as the 90th percentile of latency over 28 days2. A percentile of latency is the maximum amount of time that a given percentage of requests take to complete. For example, the 90th percentile of latency is the maximum amount of time that 90% of requests take to complete3.
To define a latency SLO, you need to consider the following factors24:
The expectations and satisfaction of your customers. You want to set a latency SLO that reflects the level of performance that your customers are happy with and willing to pay for.
The current and historical measurements of your latency. You want to set a latency SLO that is based on data and realistic for your web application.
The trade-offs and costs of improving your latency. You want to set a latency SLO that balances the benefits of faster response times with the costs of engineering work, infrastructure, and complexity.
Based on these factors, the best option for defining a latency SLO for your web application is option B. Option B sets the latency SLO to match the current measurement of your latency, which means that you are meeting the expectations and satisfaction of your customers. Option B also sets a realistic and achievable target for your web application, which means that you do not need to invest extra resources or effort to improve your latency. Option B also aligns with the best practice of setting conservative SLOs, which means that you have some buffer or margin for error in case your latency fluctuates or degrades5.
NEW QUESTION # 36
Your company runs an ecommerce website built with JVM-based applications and microservice architecture in Google Kubernetes Engine (GKE) The application load increases during the day and decreases during the night Your operations team has configured the application to run enough Pods to handle the evening peak load You want to automate scaling by only running enough Pods and nodes for the load What should you do?
- A. Configure the Vertical Pod Autoscaler and enable the cluster autoscaler
- B. Configure the Vertical Pod Autoscaler but keep the node pool size static
- C. Configure the Horizontal Pod Autoscaler but keep the node pool size static
- D. Configure the Horizontal Pod Autoscaler and enable the cluster autoscaler
Answer: D
Explanation:
Explanation
The best option for automating scaling by only running enough Pods and nodes for the load is to configure the Horizontal Pod Autoscaler and enable the cluster autoscaler. The Horizontal Pod Autoscaler is a feature that automatically adjusts the number of Pods in a deployment or replica set based on observed CPU utilization or custom metrics. The cluster autoscaler is a feature that automatically adjusts the size of a node pool based on the demand for node capacity. By using both features together, you can ensure that your application runs enough Pods to handle the load, and that your cluster runs enough nodes to host the Pods. This way, you can optimize your resource utilization and cost efficiency.
NEW QUESTION # 37
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 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
- B. 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
- C. 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
- D. 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
Answer: A
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 # 38
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. 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.
- B. Grant relevant team members read access to all GCP production projects. Create Stackdriver workspaces inside each project.
- C. Grant relevant team members the Project Viewer IAM role on all GCP production projects. Create Slackdriver workspaces inside each project.
- D. 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.
Answer: D
Explanation:
"A Project can host many Projects and appear in many Projects, but it can only be used as the scoping project once. We recommend that you create a new Project for the purpose of having multiple Projects in the same scope."
NEW QUESTION # 39
Your company is using HTTPS requests to trigger a public Cloud Run-hosted service accessible at the
https://booking-engine-abcdef .a.run.app URL You need to give developers the ability to test the latest revisions of the service before the service is exposed to customers What should you do?
- A. Grant the roles/run. invoker role to the developers testing the booking-engine service Use the https:
//booking-engine-abcdef. private. run. app URL for testing - B. Runthegcioud run deploy booking-engine -no-traffic --ag dev command Use the
https://dev----booking-engine-abcdef. a. run. app URL for testing - C. Runthegcioud run services update-traffic booking-engine -to-revisions LATEST*! command Use the ht tps: //booking-engine-abcdef. a. run. ape URL for testing
- D. Pass the curl -K "Authorization: Hearer S(gclcud auth print-identity-token)" auth token Use the https: /
/booking-engine-abcdef. a. run. app URL to test privately
Answer: C
Explanation:
Explanation
The best option for securing the CI/CD deployment pipeline is to configure vulnerability analysis with Artifact Registry and Binary Authorization. Vulnerability analysis is a feature that allows you to scan container images for known vulnerabilities and security issues. You can use vulnerability analysis with Artifact Registry, which is a service that allows you to store and manage container images and other artifacts. By using vulnerability analysis with Artifact Registry, you can ensure that your container images are scanned for vulnerabilities before they are deployed. Binary Authorization is a feature that allows you to enforce signature-based validation when deploying container images. You can use Binary Authorization with Cloud Build, which is a service that allows you to build and deploy container images. By using Binary Authorization with Cloud Build, you can ensure that only authorized and verified container images are deployed to your environment.
NEW QUESTION # 40
You are performing a semi-annual capacity planning exercise for your flagship service. You expect a service user growth rate of 10% month-over-month over the next six months. Your service is fully containerized and runs on Google Cloud Platform (GCP), using a Google Kubernetes Engine (GKE) Standard regional cluster on three zones with cluster autoscaler enabled. You currently consume about 30% of your total deployed CPU capacity, and you require resilience against the failure of a zone. You want to ensure that your users experience minimal negative impact as a result of this growth or as a result of zone failure, while avoiding unnecessary costs. How should you prepare to handle the predicted growth?
- A. Verify the maximum node pool size, enable a horizontal pod autoscaler, and then perform a load test to verify your expected resource needs.
- B. Because you are deployed on GKE and are using a cluster autoscaler, your GKE cluster will scale automatically regardless of growth rate.
- C. Proactively add 60% more node capacity to account for six months of 10% growth rate, and then perform a load test to make sure you have enough.
- D. Because you are at only 30% utilization, you have significant headroom and you won't need to add any additional capacity for this rate of growth.
Answer: B
NEW QUESTION # 41
You support a web application that runs on App Engine and uses CloudSQL and Cloud Storage for data storage. After a short spike in website traffic, you notice a big increase in latency for all user requests, increase in CPU use, and the number of processes running the application. Initial troubleshooting reveals:
After the initial spike in traffic, load levels returned to normal but users still experience high latency.
Requests for content from the CloudSQL database and images from Cloud Storage show the same high latency.
No changes were made to the website around the time the latency increased.
There is no increase in the number of errors to the users.
You expect another spike in website traffic in the coming days and want to make sure users don't experience latency. What should you do?
- A. Enable high availability on the CloudSQL instances.
- B. Upgrade the GCS buckets to Multi-Regional.
- C. Move the application from App Engine to Compute Engine.
- D. Modify the App Engine configuration to have additional idle instances.
Answer: A
NEW QUESTION # 42
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 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. - D. * 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.
Answer: B
NEW QUESTION # 43
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 Google Cloud Managed Service for Prometheus in your application
- C. Configure Error Reporting 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 # 44
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. Enable binary authorization inside the Kubernetes cluster and configure the build pipeline as an attestor.
- B. Configure the build system with protected branches that require pull request approval.
- C. Leverage Kubernetes Role-Based Access Control (RBAC) to restrict access to only approved users.
- D. Use an Admission Controller to verify that incoming requests originate from approved sources.
Answer: A
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 # 45
You support an application that stores product information in cached memory. For every cache miss, an entry is logged in Stackdriver Logging. You want to visualize how often a cache miss happens over time. What should you do?
- A. Create a logs-based metric in Stackdriver Logging and a dashboard for that metric in Stackdriver Monitoring.
- B. Link Stackdriver Logging as a source in Google Data Studio. Filler (he logs on the cache misses.
- C. Configure Stackdriver Profiler to identify and visualize when the cache misses occur based on the logs.
- D. Configure BigOuery as a sink for Stackdriver Logging. Create a scheduled query to filter the cache miss logs and write them to a separate table
Answer: A
Explanation:
Explanation
https://cloud.google.com/logging/docs/logs-based-metrics#counter-metric
NEW QUESTION # 46
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. Grant the team members the IAM role of logging.configWriter on Cloud IAM.
- B. Create and grant a custom IAM role with the permissions logging.sinks.list and logging.sink.get.
- C. Create an Organizational Policy in Cloud IAM to allow only these members to create log exports.
- D. Configure Access Context Manager to allow only these members to export logs.
Answer: A
Explanation:
Explanation/Reference: https://cloud.google.com/logging/docs/access-control
NEW QUESTION # 47
You support a user-facing web application. When analyzing the application's error budget over the previous six months, you notice that the application has never consumed more than 5% of its error budget in any given time window. You hold a Service Level Objective (SLO) review with business stakeholders and confirm that the SLO is set appropriately. You want your application's SLO to more closely reflect its observed reliability.
What steps can you take to further that goal while balancing velocity, reliability, and business needs? (Choose two.)
- A. Announce planned downtime to consume more error budget, and ensure that users are not depending on a tighter SLO.
- B. Tighten the SLO match the application's observed reliability.
- C. Add more serving capacity to all of your application's zones.
- D. Implement and measure additional Service Level Indicators (SLIs) fro the application.
- E. Have more frequent or potentially risky application releases.
Answer: A,D
Explanation:
Explanation
https://sre.google/sre-book/service-level-objectives/
You want the application's SLO to more closely reflect it's observed reliability. The key here is error budget never goes over 5%. This means they can have additional downtime and still stay within their budget.
NEW QUESTION # 48
......
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