Download Google.Professional-Cloud-DevOps-Engineer.VCEplus.2024-08-18.115q.vcex

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.

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.

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.

The correct answer is D) Grant the roles/storage.objectAdmin Identity and Access Management (IAM) role to the Cloud Build service account on the state file bucket.According to the Google Cloud documentation, Cloud Build is a service that executes your builds on Google Cloud Platform infrastructure1. Cloud Build uses a service account to execute your build steps and access resources, such as Cloud Storage buckets2. Terraform is an open-source tool that allows you to define and provision infrastructure as code3. Terraform uses a state file to store and track the state of your infrastructure4. You can configure Terraform to use a Cloud Storage bucket as a backend to store and share the state file across multiple users or environments5.The error message indicates that Cloud Build failed to access the Cloud Storage bucket that contains the Terraform state file. This is likely because the Cloud Build service account does not have the necessary permissions to read and write objects in the bucket. To resolve this issue, you need to grant the roles/storage.objectAdmin IAM role to the Cloud Build service account on the state file bucket. This role allows the service account to create, delete, and manage objects in the bucket6. You can use the gcloud command-line tool or the Google Cloud Console to grant this role.The other options are incorrect because they do not follow Google-recommended practices. Option A is incorrect because it changes the Terraform code to use local state, which is not recommended for production or collaborative environments, as it can cause conflicts, data loss, or inconsistency. Option B is incorrect because it creates a new storage bucket with the name specified in the Terraform configuration, but it does not grant any permissions to the Cloud Build service account on the new bucket. Option C is incorrect because it grants the roles/owner IAM role to the Cloud Build service account on the project, which is too broad and violates the principle of least privilege. The roles/owner role grants full access to all resources in the project, which can pose a security risk if misused or compromised.Cloud Build Documentation, Overview. Service accounts, Service accounts. Terraform by HashiCorp, Terraform by HashiCorp. State, State. Google Cloud Storage Backend, Google Cloud Storage Backend. Predefined roles, Predefined roles. [Granting roles to service accounts for specific resources], Granting roles to service accounts for specific resources. [Local Backend], Local Backend. [Understanding roles], Understanding roles.

The correct answer is C) Configure Cloud Profiler, and initialize the cloud.google.com/go/profiler library in the application.According to the Google Cloud documentation, Cloud Profiler is a statistical, low-overhead profiler that continuously gathers CPU usage and memory-allocation information from your production applications1. Cloud Profiler can help you identify slow paths in your code and optimize the performance of your applications. Cloud Profiler supports applications written in Go that run on App Engine standard environment2. To use Cloud Profiler, you need to configure it in your Google Cloud project and initialize the cloud.google.com/go/profiler library in your application code3. You can then use the Cloud Profiler interface to analyze the profiling data and visualize the results by using flame graphs4. Cloud Profiler has minimal performance impact and management overhead, as it only samples a small fraction of the application activity and does not require any additional infrastructure or agents.The other options are incorrect because they do not meet the requirements of minimizing performance impact and management overhead. Option A is incorrect because it requires installing a continuous profiling tool into Compute Engine, which is an additional infrastructure that needs to be managed and maintained. Option B is incorrect because it requires periodically running the go tool pprof command against the application instance, which is a manual and disruptive process that can affect the application performance. Option D is incorrect because it only uses Cloud Monitoring to assess the App Engine CPU utilization metric, which is not enough to identify slow paths in the code or optimize the application performance.Cloud Profiler documentation, Overview. Profiling Go applications, Supported environments. Profiling Go applications, Using Cloud Profiler. Analyzing data, Analyzing data.

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.

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.Anthos Config Management documentation, Overview. Policy Controller, Policy Controller. Constraint template library, Constraint template library. Installing Anthos Config Management, Installing Anthos Config Management.Syncing configurations, Syncing configurations. Cloud Build documentation, Overview. Anthos Service Mesh documentation, Overview. [Config Connector documentation], Overview.

Terraform is an open-source tool that allows you to define and provision infrastructure as code1.Terraform can be used to create and manage Google Cloud resources, such as projects, networks, and services2.The Cloud Foundation Toolkit is a set of open-source Terraform modules and tools that provide best practices and guidance for deploying Google Cloud infrastructure3.The Cloud Foundation Toolkit includes Terraform repositories for creating Google Cloud projects and related resources, such as IAM policies, APIs, service accounts, and billing4. By using the Terraform repositories from the Cloud Foundation Toolkit, you can design a fast, reliable, and repeatable solution for your company to provision new projects and basic resources in Google Cloud. You can also customize the Terraform code to suit your specific needs and preferences.

Cloud Build is a service that executes your builds on Google Cloud Platform infrastructure1.Cloud Build can be used as a pipeline runner for your CI pipeline, which is a process that automates the integration and testing of your code2.Cloud Build private pools are private, dedicated pools of workers that offer greater customization over the build environment, including the ability to access resources in a private VPC network3. A VPC network is a virtual network that provides connectivity for your Google Cloud resources and services.By using Cloud Build private pools, you can implement a solution that minimizes management overhead, as Cloud Build private pools are hosted and fully-managed by Cloud Build and scale up and down to zero, with no infrastructure to set up, upgrade, or scale3. You can also implement a solution that meets your security requirement, as Cloud Build private pools use network peering to connect into your private VPC network and do not expose API traffic publicly.

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) = $900Since 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.