Professional-Cloud-DevOps-Engineer Exam Dumps - Google Cloud DevOps Engineer Questions and Answers

Google Workload Identity is the recommended method to allow GKE workloads to securely access Google Cloud APIs using short-lived credentials without managing keys.

"Workload Identity is the recommended way to access Google Cloud services from GKE. It replaces the older method of using service account keys."

— Workload Identity Overview

"You can configure a Kubernetes service account to impersonate a Google Cloud service account by granting the roles/iam.workloadIdentityUser role and using the iam.gke.io/gcp-service-account annotation."

— Configure Workload Identity

This satisfies the customer’s requirement for PoLP and avoids long-lived keys.

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.

 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.

Your CTO has asked you to implement a postmortem policy on every incident for internal use. You want to define what a good postmortem is to ensure that the policy is successful at your company. What should you do?

Choose 2 answers

Ensure that all postmortems include what caused the incident, identify the person or team responsible for

causing the incident. and how to prevent a future occurrence of the incident.

Ensure that all postmortems include what caused the incident, how the incident could have been worse, and how to prevent a future occurrence of the incident.

Ensure that all postmortems include the severity of the incident, how to prevent a future occurrence of the incident. and what caused the incident without naming internal system components.

Ensure that all postmortems include how the incident was resolved and what caused the incident without naming customer information.

Ensure that all postmortems include all incident participants in postmortem authoring and share postmortems as widely as possible,

Answer: BE

The correct answers are B and E.

A good postmortem should include what caused the incident, how the incident could have been worse, and how to prevent a future occurrence of the incident1. This helps to identify the root cause of the problem, the impact of the incident, and the actions to take to mitigate or eliminate the risk of recurrence.

A good postmortem should also include all incident participants in postmortem authoring and share postmortems as widely as possible2. This helps to foster a culture of learning and collaboration, as well as to increase the visibility and accountability of the incident response process.

Answer A is incorrect because it assigns blame to a person or team, which goes against the principle of blameless postmortems2. Blameless postmortems focus on finding solutions rather than pointing fingers, and encourage honest and constructive feedback without fear of punishment.

Answer C is incorrect because it omits how the incident could have been worse, which is an important factor to consider when evaluating the severity and impact of the incident1. It also avoids naming internal system components, which makes it harder to understand the technical details and root cause of the problem.

Answer D is incorrect because it omits how to prevent a future occurrence of the incident, which is the main goal of a postmortem1. It also avoids naming customer information, which may be relevant for understanding the impact and scope of the incident.

Your uses Jenkins running on Google Cloud VM instances for CI/CD. You need to extend the functionality to use infrastructure as code automation by using Terraform. You must ensure that the Terraform Jenkins instance is authorized to create Google Cloud resources. You want to follow Google-recommended practices- What should you do?

Add the auth application-default command as a step in Jenkins before running the Terraform commands.

Create a dedicated service account for the Terraform instance. Download and copy the secret key value to the GOOGLE environment variable on the Jenkins server.

Confirm that the Jenkins VM instance has an attached service account with the appropriate Identity and Access Management (IAM) permissions.

use the Terraform module so that Secret Manager can retrieve credentials.

Answer: C

The correct answer is C.

Confirming that the Jenkins VM instance has an attached service account with the appropriate Identity and Access Management (IAM) permissions is the best way to ensure that the Terraform Jenkins instance is authorized to create Google Cloud resources.This follows the Google-recommended practice of using service accounts to authenticate and authorize applications running on Google Cloud1.Service accounts are associated with private keys that can be used to generate access tokens for Google Cloud APIs2.By attaching a service account to the Jenkins VM instance, Terraform can use the Application Default Credentials (ADC) strategy to automatically find and use the service account credentials3.

Answer A is incorrect because the auth application-default command is used to obtain user credentials, not service account credentials.User credentials are not recommended for applications running on Google Cloud, as they are less secure and less scalable than service account credentials1.

Answer B is incorrect because it involves downloading and copying the secret key value of the service account, which is not a secure or reliable way of managing credentials.The secret key value should be kept private and not exposed to any other system or user2. Moreover, setting the GOOGLE environment variable on the Jenkins server is not a valid way of providing credentials to Terraform.Terraform expects the credentials to be either in a file pointed by the GOOGLE_APPLICATION_CREDENTIALS environment variable, or in a provider block with the credentials argument3.

Answer D is incorrect because it involves using the Terraform module for Secret Manager, which is a service that stores and manages sensitive data such as API keys, passwords, and certificates. While Secret Manager can be used to store and retrieve credentials, it is not necessary or sufficient for authorizing the Terraform Jenkins instance. The Terraform Jenkins instance still needs a service account with the appropriate IAM permissions to access Secret Manager and other Google Cloud resources.

You are analyzing Java applications in production. All applications have Cloud Profiler and Cloud Trace installed and configured by default. You want to determine which applications need performance tuning. What should you do?

Choose 2 answers

A. Examine the wall-clock time and the CPU time Of the application. If the difference is substantial, increase the CPU resource allocation.

B. Examine the wall-clock time and the CPU time of the application. If the difference is substantial, increase the memory resource allocation.

C. 17 Examine the wall-clock time and the CPU time of the application. If the difference is substantial, increase the local disk storage allocation.

D. O Examine the latency time, the wall-clock time, and the CPU time of the application. If the latency time is slowly burning down the error budget, and the difference between wall-clock time and CPU time is minimal, mark the application for optimization.

E. Examine the heap usage Of the application. If the usage is low, mark the application for optimization.

Answer: AD

The correct answers are A and D.

Examine the wall-clock time and the CPU time of the application. If the difference is substantial, increase the CPU resource allocation. This is a good way to determine if the application is CPU-bound, meaning that it spends more time waiting for the CPU than performing actual computation.Increasing the CPU resource allocation can improve the performance of CPU-bound applications1.

Examine the latency time, the wall-clock time, and the CPU time of the application. If the latency time is slowly burning down the error budget, and the difference between wall-clock time and CPU time is minimal, mark the application for optimization. This is a good way to determine if the application is I/O-bound, meaning that it spends more time waiting for input/output operations than performing actual computation.Increasing the CPU resource allocation will not help I/O-bound applications, and they may need optimization to reduce the number or duration of I/O operations2.

Answer B is incorrect because increasing the memory resource allocation will not help if the application is CPU-bound or I/O-bound. Memory allocation affects how much data the application can store and access in memory, but it does not affect how fast the application can process that data.

Answer C is incorrect because increasing the local disk storage allocation will not help if the application is CPU-bound or I/O-bound. Disk storage affects how much data the application can store and access on disk, but it does not affect how fast the application can process that data.

Answer E is incorrect because examining the heap usage of the application will not help to determine if the application needs performance tuning. Heap usage affects how much memory the application allocates for dynamic objects, but it does not affect how fast the application can process those objects. Moreover, low heap usage does not necessarily mean that the application is inefficient or unoptimized.

You deployed an application into a large Standard Google Kubernetes Engine (GKE) cluster. The application is stateless and multiple pods run at the same time. Your application receives inconsistent traffic. You need to ensure that the user experience remains consistent regardless of changes in traffic. and that the resource usage of the cluster is optimized.

What should you do?

Configure a cron job to scale the deployment on a schedule.

Configure a Horizontal Pod Autoscaler.

Configure a Vertical Pod Autoscaler.

Configure cluster autoscaling on the node pool.

Answer: B

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.

The correct answer is C. Configure Anthos Config Management with the GitHub repository. When there is a change in the repository, use Anthos Config Management to apply the change.

According to the web search results, Anthos Config Management is a service that lets you manage the configuration of your Google Kubernetes Engine (GKE) clusters from a single source of truth, such as a GitHub repository1. Anthos Config Management can enforce several constraint templates across your GKE clusters by using Policy Controller, which is a feature that integrates the Open Policy Agent (OPA) Constraint Framework into Anthos Config Management2. Policy Controller can apply constraints that include policy parameters, such as restricting the Kubernetes API3. To use Anthos Config Management and Policy Controller, you need to configure them with your GitHub repository and enable the sync mode4. When there is a change in the repository, Anthos Config Management will automatically sync and apply the change to your GKE clusters5.

The other options are incorrect because they do not use Anthos Config Management and Policy Controller. Option A is incorrect because it uses a GitHub action to trigger Cloud Build, which is a service that executes your builds on Google Cloud Platform infrastructure6. Cloud Build can run a gcloud CLI command to apply the change, but it does not use Anthos Config Management or Policy Controller. Option B is incorrect because it uses a web hook to send a request to Anthos Service Mesh, which is a service that provides a uniform way to connect, secure, monitor, and manage microservices on GKE clusters7. Anthos Service Mesh can apply the change, but it does not use Anthos Config Management or Policy Controller. Option D is incorrect because it uses Config Connector, which is a service that lets you manage Google Cloud resources through Kubernetes configuration. Config Connector can apply the change, but it does not use Anthos Config Management or Policy Controller.

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/

https://cloud.google.com/container-registry/docs/pushing-and-pulling

The best option for implementing a scalable solution to support global Prometheus querying and minimize management overhead is to use Google Cloud Managed Service for Prometheus. Google Cloud Managed Service for Prometheus is a fully managed service that allows you to collect, query, and visualize metrics from your GKE clusters using Prometheus-based tooling. You can use Google Cloud Managed Service for Prometheus to query metrics across multiple clusters and regions using a global view. You can also use Google Cloud Managed Service for Prometheus to integrate with other Google Cloud services, such as Cloud Monitoring, Cloud Logging, and BigQuery. By using Google Cloud ManagedService for Prometheus, you can avoid managing and scaling your own Prometheus servers and focus on your application performance.

The most effective ways to measure application reliability from a user perspective without making any engineering changes are to create new synthetic clients to simulate a user journey using the application, and to use current and historic Request Logs to trace customer interaction with the application. These methods can help you monitor the availability, latency, and errors of your application from an end-user perspective .

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.