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NEW QUESTION # 66
You are designing a deployment technique for your applications on Google Cloud. As part Of your deployment planning, you want to use live traffic to gather performance metrics for new versions Of your applications. You need to test against the full production load before your applications are launched. What should you do?
- A. Use shadow testing with continuous deployment.
- B. Use canary testing with continuous deployment.
- C. Use A/B testing with blue/green deployment.
- D. Use canary testing with rolling updates deployment,
Answer: A
Explanation:
The correct answer is B, Use shadow testing with continuous deployment.
Shadow testing is a deployment technique that involves routing a copy of the live traffic to a new version of the application, without affecting the production environment. This way, you can gather performance metrics and compare them with the current version, without exposing the new version to the users. Shadow testing can help you test against the full production load and identify any issues or bottlenecks before launching the new version. You can use continuous deployment to automate the process of deploying the new version after it passes the shadow testing.
Reference:
Application deployment and testing strategies, Testing strategies, Shadow test pattern.
NEW QUESTION # 67
You encountered a major service outage that affected all users of the service for multiple hours. After several hours of incident management, the service returned to normal, and user access was restored. You need to provide an incident summary to relevant stakeholders following the Site Reliability Engineering recommended practices. What should you do first?
- A. Require the engineer responsible to write an apology email to all stakeholders.
- B. Send the Incident State Document to all the stakeholders.
- C. Call individual stakeholders lo explain what happened.
- D. Develop a post-mortem to be distributed to stakeholders.
Answer: D
NEW QUESTION # 68
You are the on-call Site Reliability Engineer for a microservice that is deployed to a Google Kubernetes Engine (GKE) Autopilot cluster. Your company runs an online store that publishes order messages to Pub/Sub and a microservice receives these messages and updates stock information in the warehousing system. A sales event caused an increase in orders, and the stock information is not being updated quickly enough. This is causing a large number of orders to be accepted for products that are out of stock You check the metrics for the microservice and compare them to typical levels.
You need to ensure that the warehouse system accurately reflects product inventory at the time orders are placed and minimize the impact on customers What should you do?
- A. Decrease the acknowledgment deadline on the subscription
- B. Increase the Pod CPU and memory limits
- C. Add a virtual queue to the online store that allows typical traffic levels
- D. Increase the number of Pod replicas
Answer: D
Explanation:
Explanation
The best option for ensuring that the warehouse system accurately reflects product inventory at the time orders are placed and minimizing the impact on customers is to increase the number of Pod replicas. Increasing the number of Pod replicas will increase the scalability and availability of your microservice, which will allow it to handle more Pub/Sub messages and update stock information faster. This way, you can reduce the backlog of undelivered messages and oldest unacknowledged message age, which are causing delays in updating product inventory. You can use Horizontal Pod Autoscaler or Cloud Monitoring metrics-based autoscaling to automatically adjust the number of Pod replicas based on load or custom metrics.
NEW QUESTION # 69
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 you minimize costs Different teams should not be able to access other teams environments You want to follow Google-recommended practices What should you do?
- A. Create one Google Cloud project per team In each project create a cluster with a Kubernetes namespace for development and one for production Grant the teams Identity and Access Management (1AM) access to their respective clusters.
- B. 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
- C. Create one Google Cloud project per team In each project create a cluster for development and one for production Grant the teams Identity and Access Management (1AM) access to their respective clusters
- D. 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
Answer: D
Explanation:
The best option for creating the development and production environments for each team while minimizing costs and ensuring isolation is to 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. This option allows you to use fewer clusters and projects than creating one project or cluster per team, which reduces costs and complexity. It also allows you to isolate each team's environment by using namespaces and RBAC, which prevents teams from accessing other teams' environments.
NEW QUESTION # 70
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: 10
MTBF: 90
Impact 50% - B. MTTD:5
MTTR: 20
MTBF: 90
Impact: 50% - C. MTTD: 5
MTTR: 10
MTBF: 90
Impact: 33% - D. MTTD:5
MTTR: 20
MTBF: 90
Impact: 33%
Answer: A
NEW QUESTION # 71
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. Establish a communication channel where incident responders and leads can communicate with each other.
- B. Look for ways to mitigate user impact and deploy the mitigations to production.
- C. Contact the affected service owners and update them on the status of the incident.
- D. Start a postmortem, add incident information, circulate the draft internally, and ask internal stakeholders for input.
Answer: B
NEW QUESTION # 72
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 services update-traffic booking-engine -to-revisions LATEST*! command Use the ht tps: //booking-engine-abcdef. a. run. ape URL for testing
- C. Runthegcioud run deploy booking-engine -no-traffic --ag dev command Use the https://dev----booking-engine-abcdef. a. run. app 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: B
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 # 73
You support a service with a well-defined Service Level Objective (SLO). Over the previous 6 months, your service has consistently met its SLO and customer satisfaction has been consistently high. Most of your service's operations tasks are automated and few repetitive tasks occur frequently. You want to optimize the balance between reliability and deployment velocity while following site reliability engineering best practices. What should you do? (Choose two.)
- A. Shift engineering time to other services that need more reliability.
- B. Change the implementation of your Service Level Indicators (SLIs) to increase coverage.
- C. Get the product team to prioritize reliability work over new features.
- D. Increase the service's deployment velocity and/or risk.
- E. Make the service's SLO more strict.
Answer: A,D
Explanation:
(https://sre.google/workbook/implementing-slos/#slo-decision-matrix)
NEW QUESTION # 74
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. Supply the source control tag as a parameter within the image name.
- B. Use Cloud Build to include the release version tag in the application image.
- C. Use GCR digest versioning to match the image to the tag in source control.
- D. Reference the image digest in the source control tag.
Answer: A
Explanation:
Explanation
https://cloud.google.com/container-registry/docs/pushing-and-pulling
NEW QUESTION # 75
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. Create a custom builder for Cloud Build that will only push images to gcr.io/altostrat-images.
- D. Add a tag to each image in gcr.io/altostrat-images and check that this tag is present when the image is deployed.
Answer: A
NEW QUESTION # 76
You use Cloud Build to build your application. You want to reduce the build time while minimizing cost and development effort. What should you do?
- A. Use larger Cloud Build virtual machines (VMs) by using the machine-type option.
- B. Use multiple smaller build steps to minimize execution time.
- C. Use Cloud Storage to cache intermediate artifacts.
- D. Run multiple Jenkins agents to parallelize the build.
Answer: B
NEW QUESTION # 77
You support an application deployed on Compute Engine. The application connects to a Cloud SQL instance to store and retrieve dat a. After an update to the application, users report errors showing database timeout messages. The number of concurrent active users remained stable. You need to find the most probable cause of the database timeout. What should you do?
- A. Check the serial port logs of the Compute Engine instance.
- B. Use Stackdriver Profiler to visualize the resources utilization throughout the application.
- C. Use Cloud Security Scanner to see whether your Cloud SQL is under a Distributed Denial of Service (DDoS) attack.
- D. Determine whether there is an increased number of connections to the Cloud SQL instance.
Answer: D
NEW QUESTION # 78
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 with a Kubernetes namespace for Development and one for Production. Grant the teams IAM access to their respective clusters.
- B. 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.
- C. 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.
- 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: B
NEW QUESTION # 79
Your organization uses a change advisory board (CAB) to approve all changes to an existing service You want to revise this process to eliminate any negative impact on the software delivery performance What should you do?
Choose 2 answers
- A. Replace the CAB with a senior manager to ensure continuous oversight from development to deployment
- B. Batch changes into larger but less frequent software releases
- C. Let developers merge their own changes but ensure that the team's deployment platform can roll back changes if any issues are discovered
- D. Move to a peer-review based process for individual changes that is enforced at code check-in time and supported by automated tests
- E. Ensure that the team's development platform enables developers to get fast feedback on the impact of their changes
Answer: D,E
Explanation:
Explanation
A change advisory board (CAB) is a traditional way of approving changes to a service, but it can slow down the software delivery performance and introduce bottlenecks. A better way to improve the speed and quality of changes is to use a peer-review based process for individual changes that is enforced at code check-in time and supported by automated tests. This way, developers can get fast feedback on the impact of their changes and catch any errors or bugs before they reach production. Additionally, the team's development platform should enable developers to get fast feedback on the impact of their changes, such as using Cloud Code, Cloud Build, or Cloud Debugger.
NEW QUESTION # 80
You manage an application that runs in Google Kubernetes Engine (GKE) and uses the blue/green deployment methodology Extracts of the Kubernetes manifests are shown below
The Deployment app-green was updated to use the new version of the application During post-deployment monitoring you notice that the majority of user requests are failing You did not observe this behavior in the testing environment You need to mitigate the incident impact on users and enable the developers to troubleshoot the issue What should you do?
- A. Change the selector on the Service app-2vc to app: my-app.
- B. Change the selector on the Service app-svc to app: my-app, version: blue
- C. Update the Deployment ape-green to use the previous version of the application
- D. Update the Deployment app-blue to use the new version of the application
Answer: B
Explanation:
Explanation
The best option for mitigating the incident impact on users and enabling the developers to troubleshoot the issue is to change the selector on the Service app-svc to app: my-app, version: blue. A Service is a resource that defines how to access a set of Pods. A selector is a field that specifies which Pods are selected by the Service. By changing the selector on the Service app-svc to app: my-app, version: blue, you can ensure that the Service only routes traffic to the Pods that have both labels app: my-app and version: blue. These Pods belong to the Deployment app-blue, which uses the previous version of the application. This way, you can mitigate the incident impact on users by switching back to the working version of the application. You can also enable the developers to troubleshoot the issue with the new version of the application in the Deployment app-green without affecting users.
NEW QUESTION # 81
You are responsible for the reliability of a high-volume enterprise application. A large number of users report that an important subset of the application's functionality - a data intensive reporting feature - is consistently failing with an HTTP 500 error. When you investigate your application's dashboards, you notice a strong correlation between the failures and a metric that represents the size of an internal queue used for generating reports. You trace the failures to a reporting backend that is experiencing high I/O wait times. You quickly fix the issue by resizing the backend's persistent disk (PD). How you need to create an availability Service Level Indicator (SLI) for the report generation feature. How would you define it?
- A. As the proportion of report generation requests that result in a successful response
- B. As the application's report generation queue size compared to a known-good threshold
- C. As the I/O wait times aggregated across all report generation backends
- D. As the reporting backend PD throughout capacity compared to a known-good threshold
Answer: A
Explanation:
Explanation
According to SRE Workbook, one of potential SLI is as below:
* Type of service: Request-driven
* Type of SLI: Availability
* Description: The proportion of requests that resulted in a successful response.
https://sre.google/workbook/implementing-slos/
NEW QUESTION # 82
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 a latency metric for a window-based method of evaluation.
- B. Select an availability metric for a window-based method Of evaluation.
- C. Select an availability metric for a request-based method of evaluation.
- D. Select a latency metric for a request-based method of evaluation.
Answer: D
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 # 83
You are managing the production deployment to a set of Google Kubernetes Engine (GKE) clusters. You want to make sure only images which are successfully built by your trusted CI/CD pipeline are deployed to production. What should you do?
- A. Enable Vulnerability Analysis on the Container Registry.
- B. Set up the Kubernetes Engine clusters with Binary Authorization.
- C. Enable Cloud Security Scanner on the clusters.
- D. Set up the Kubernetes Engine clusters as private clusters.
Answer: D
NEW QUESTION # 84
You are building an application that runs on Cloud Run The application needs to access a third-party API by using an API key You need to determine a secure way to store and use the API key in your application by following Google-recommended practices What should you do?
- A. Save the API key in Secret Manager as a secret Reference the secret as an environment variable in the Cloud Run application
- B. Save the API key in Secret Manager as a secret key Mount the secret key under the /sys/api_key directory and decrypt the key in the Cloud Run application
- C. Encrypt the API key by using Cloud Key Management Service (Cloud KMS) and pass the key to Cloud Run as an environment variable Decrypt and use the key in Cloud Run
- D. Save the API key in Cloud Key Management Service (Cloud KMS) as a key Reference the key as an environment variable in the Cloud Run application
Answer: A
Explanation:
The best option for storing and using the API key in your application by following Google-recommended practices is to save the API key in Secret Manager as a secret and reference the secret as an environment variable in the Cloud Run application. Secret Manager is a service that allows you to store and manage sensitive data, such as API keys, passwords, and certificates, in Google Cloud. A secret is a resource that represents a logical secret, such as an API key. You can save the API key in Secret Manager as a secret and use IAM policies to control who can access it. You can also reference the secret as an environment variable in the Cloud Run application by using the ${SECRET_NAME} syntax. This way, you can securely store and use the API key in your application without exposing it in your code or configuration files.
NEW QUESTION # 85
You recently noticed that one Of your services has exceeded the error budget for the current rolling window period. Your company's product team is about to launch a new feature. You want to follow Site Reliability Engineering (SRE) practices.
What should you do?
- A. Escalate the situation and request additional error budget.
- B. Look through other metrics related to the product and find SLOs with remaining error budget. Reallocate the error budgets and allow the feature launch.
- C. Notify the team about the lack of error budget and ensure that all their tests are successful so the launch will not further risk the error budget.
- D. Notify the team that their error budget is used up. Negotiate with the team for a launch freeze or tolerate a slightly worse user experience.
Answer: D
Explanation:
The correct answer is
A, Notify the team that their error budget is used up. Negotiate with the team for a launch freeze or tolerate a slightly worse user experience.
According to the Site Reliability Engineering (SRE) practices, an error budget is the amount of unreliability that a service can tolerate without harming user satisfaction1. An error budget is derived from the service-level objectives (SLOs), which are the measurable goals for the service quality2. When a service exceeds its error budget, it means that it has violated its SLOs and may have negatively impacted the users. In this case, the SRE team should notify the product team that their error budget is used up and negotiate with them for a launch freeze or a lower SLO3. A launch freeze means that no new features are deployed until the service reliability is restored. A lower SLO means that the product team accepts a slightly worse user experience in exchange for launching new features. Both options require a trade-off between reliability and innovation, and should be agreed upon by both teams.
The other options are incorrect because they do not follow the SRE practices. Option B is incorrect because it violates the principle of error budget autonomy, which means that each service should have its own error budget and SLOs, and should not borrow or reallocate them from other services4. Option C is incorrect because it does not address the root cause of the error budget overspend, and may create unrealistic expectations for the service reliability. Option D is incorrect because it does not prevent the possibility of introducing new errors or bugs with the feature launch, which may further degrade the service quality and user satisfaction.
Reference:
Error Budgets, Error Budgets. Service Level Objectives, Service Level Objectives. Error Budget Policies, Error Budget Policies. Error Budget Autonomy, Error Budget Autonomy.
NEW QUESTION # 86
You have a set of applications running on a Google Kubernetes Engine (GKE) cluster, and you are using Stackdriver Kubernetes Engine Monitoring. You are bringing a new containerized application required by your company into production. This application is written by a third party and cannot be modified or reconfigured. The application writes its log information to /var/log/app_messages.log, and you want to send these log entries to Stackdriver Logging. What should you do?
- A. Install Kubernetes on Google Compute Engine (GCE> and redeploy your applications. Then customize the built-in Stackdriver Logging configuration to tail the log file in the application's pods and write to Stackdriver Logging.
- B. Write a script to tail the log file within the pod and write entries to standard output. Run the script as a sidecar container with the application's pod. Configure a shared volume between the containers to allow the script to have read access to /var/log in the application container.
- C. Deploy a Fluentd daemonset to GKE. Then create a customized input and output configuration to tail the log file in the application's pods and write to Slackdriver Logging.
- D. Use the default Stackdriver Kubernetes Engine Monitoring agent configuration.
Answer: C
NEW QUESTION # 87
You work for a global organization and are running a monolithic application on Compute Engine You need to select the machine type for the application to use that optimizes CPU utilization by using the fewest number of steps You want to use historical system metncs to identify the machine type for the application to use You want to follow Google-recommended practices What should you do?
- A. Use the Recommender API and apply the suggested recommendations
- B. Install the Ops Agent in a fleet of VMs by using the gcloud CLI
- C. Review the Cloud Monitoring dashboard for the VM and choose the machine type with the lowest CPU utilization
- D. Create an Agent Policy to automatically install Ops Agent in all VMs
Answer: A
Explanation:
Explanation
The best option for selecting the machine type for the application to use that optimizes CPU utilization by using the fewest number of steps is to use the Recommender API and apply the suggested recommendations.
The Recommender API is a service that provides recommendations for optimizing your Google Cloud resources, such as Compute Engine instances, disks, and firewalls. You can use the Recommender API to get recommendations for changing the machine type of your Compute Engine instances based on historical system metrics, such as CPU utilization. You can also apply the suggested recommendations by using the Recommender API or Cloud Console. This way, you can optimize CPU utilization by using the most suitable machine type for your application with minimal effort.
NEW QUESTION # 88
As a Site Reliability Engineer, you support an application written in GO that runs on Google Kubernetes Engine (GKE) in production. After releasing a new version Of the application, you notice the application runs for about 15 minutes and then restarts. You decide to add Cloud Profiler to your application and now notice that the heap usage grows constantly until the application restarts. What should you do?
- A. Increase the memory limit in the application deployment.
- B. Add high memory compute nodes to the cluster.
- C. Increase the CPU limit in the application deployment.
- D. Add Cloud Trace to the application, and redeploy.
Answer: A
Explanation:
Explanation
The correct answer is B. Increase the memory limit in the application deployment.
The application is experiencing a memory leak, which means that it is allocating memory that is not freed or reused. This causes the heap usage to grow constantly until it reaches the memory limit of the pod, which triggers a restart by Kubernetes. Increasing the memory limit in the application deployment can help mitigate the problem by allowing the application to run longer before reaching the limit. However, this is not a permanent solution, as the memory leak will still occur and eventually exhaust the available memory. The best solution is to identify and fix the source of the memory leak in the application code, using tools like Cloud Profiler and pprof12.
NEW QUESTION # 89
......
To be eligible for the Google Professional-Cloud-DevOps-Engineer exam, candidates must have at least three years of experience in DevOps engineering, as well as a deep understanding of Google Cloud technologies. They should also have experience with continuous delivery and other DevOps practices, such as automation, infrastructure as code, and monitoring, among others.
Introduction to Google Professional Cloud DevOps Engineer Exam
Google Professional Cloud DevOps Engineer Exam is a certification exam that is conducted by Google to validates candidate knowledge and skills of working as a Professional Cloud DevOps engineer in the IT industry.
After passing this Professional Cloud DevOps Engineer Exam test, candidates get a certificate from Google that helps them to demonstrate their proficiency in Google Professional Cloud DevOps Engineer to their clients and employers.
Google Professional-Cloud-DevOps-Engineer exam is a certification program designed to validate the skills and knowledge of professionals in the field of DevOps engineering on the Google Cloud Platform. Professional-Cloud-DevOps-Engineer exam is intended for individuals who are experienced in DevOps practices and have expertise in designing, building, and maintaining scalable and reliable cloud solutions.
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