Google Professional-Cloud-DevOps-Engineer Google Cloud Certified - Professional Cloud DevOps Engineer Exam Exam Practice Test

The correct answer is D. Option D.

According to the DevOps best practices, a joint-ownership model for a service between the DevOps team and the Software Development team should follow these principles12:

• The DevOps team and the Software Development team should share the responsibility and collaboration for managing the service infrastructure, performing code reviews, and adopting and sharing SLOs for the service.
• The DevOps team and the Software Development team should have end-to-end ownership of the service, from design to development to deployment to operation to maintenance.
• The DevOps team and the Software Development team should use common tools and processes to facilitate communication, coordination, and feedback.
• The DevOps team and the Software Development team should align their goals and incentives with the business outcomes and customer satisfaction.

Option D is the only option that reflects these principles. Option D assigns both teams the responsibilities of managing the service infrastructure, performing code reviews, and adopting and sharing SLOs for the service. Option D also implies that both teams have end-to-end ownership of the service, as they are involved in every stage of the service lifecycle. Option D also encourages both teams to use common tools and processes, such as GitLab3, to collaborate and communicate effectively. Option D also aligns both teams with the business outcomes and customer satisfaction, as they use SLOs to measure and improve the service quality.

The other options are incorrect because they do not follow the DevOps best practices. Option A is incorrect because it assigns only the DevOps team the responsibility of managing the service infrastructure, which creates a silo between the two teams and reduces their collaboration. Option A also does not assign any responsibility for adopting and sharing SLOs for the service, which means that both teams lack a common metric for measuring and improving the service quality. Option B is incorrect because it assigns only the Software Development team the responsibility of performing code reviews, which creates a gap between the two teams and reduces their feedback. Option B also does not assign any responsibility for adopting and sharing SLOs for the service, which means that both teams lack a common metric for measuring and improving the service quality. Option C is incorrect because it assigns both teams the same responsibilities as option A and option B, which combines their drawbacks.

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/blog/topics/retail/preparing-for-peak-holiday-season-while-wfh

The best options for defining triggers that indicate whether an incident requires a postmortem based on Site Reliability Engineering (SRE) practices are an external stakeholder asks for a postmortem and data is lost due to an incident. An external stakeholder is someone who is affected by or has an interest in the service, such as a customer or a partner. If an external stakeholder asks for a postmortem, it means that they are concerned about the impact or root cause of the incident, and they expect an explanation and remediation from the service provider. Therefore, this should trigger a postmortem to address their concerns and improve their satisfaction. Data loss is a serious consequence of an incident that can affect the integrity and reliability of the service. If data is lost due to an incident, it means that there was a failure in the backup or recovery mechanisms, or that there was a corruption or deletion of data. Therefore, this should trigger a postmortem to investigate the cause and impact of the data loss, and to prevent it from happening again.

https://cloud.google.com/logging/docs/routing/overview

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 .

https://www.atlassian.com/incident-management/kpis/common-metrics

https://linkedin.github.io/school-of-sre/

The best option for giving developers the ability to test the latest revisions of the service before the service is exposed to customers is to run the gcloud run deploy booking-engine --no-traffic --tag dev command and use the https://dev----booking-engine-abcdef.a.run.app URL for testing. The gcloud run deploy command is a command that deploys a new revision of your service or updates an existing service. By using the --no-traffic flag, you can prevent any traffic from being sent to the new revision. By using the --tag flag, you can assign a tag to the new revision, such as dev. This way, you can create a new revision of your service without affecting your customers. You can also use the tag-based URL (e.g., https://dev----booking-engine-abcdef.a.run.app) to access and test the new revision.

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

Google always aims to first stop the impact of an incident, and then find the root cause (unless the root cause just happens to be identified early on).

The correct answer is B. Create an attestation for the builds that pass the load test by using a private key stored in Cloud Key Management Service (Cloud KMS) authenticated through Workload Identity.

According to the Google Cloud documentation, Binary Authorization is a deploy-time security control that ensures only trusted container images are deployed on Google Kubernetes Engine (GKE) or Cloud Run1. Binary Authorization uses attestations to certify that a specific image has completed a previous stage in the CI/CD pipeline, such as passing a load test2. Attestations are signed by private keys that are associated with attestors, which are entities that verify the attestations3. To follow Google-recommended practices, you should store your private keys in Cloud Key Management Service (Cloud KMS), which is a secure and scalable service for managing cryptographic keys4. You should also use Workload Identity, which is a feature that allows Kubernetes service accounts to act as Google service accounts, to authenticate to Cloud KMS and sign attestations without having to manage or expose service account keys5.

The other options are incorrect because they do not follow Google-recommended practices. Option A and option D require human intervention to sign the attestations, which is not scalable or automated. Option C exposes the service account JSON key as a Kubernetes Secret, which is less secure than using Workload Identity.

 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 configuring Cloud Logging to log each IP address that accesses the API by using the fewest number of steps is to enable logging on the firewall rule. A firewall rule is a rule that controls the traffic to and from your Compute Engine instances. You can enable logging on a firewall rule to capture information about the traffic that matches the rule, such as source and destination IP addresses, protocols, ports, and actions. You can use Cloud Logging to view and export the firewall logs to other destinations, such as BigQuery, for further analysis.

https://devops.com/when-it-disaster-strikes-part-3-conducting-a-blameless-post-mortem/

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 B. Cloud Infrastructure (Terraform) repository is shared: different directories are different environments. GKE Infrastructure (Anthos Config Management Kustomize manifests) repositories are separated: different branches are different environments. Application (app source code) repositories are separated: different branches are different features.

This answer follows the best practices for using Terraform and Anthos Config Management with GitOps, as described in the following sources:

• For Terraform, it is recommended to use a single repository for all environments, and use directories to separate them. This way, you can reuse the same Terraform modules and configurations across environments, and avoid code duplication and drift. You can also use Terraform workspaces to isolate the state files for each environment12.
• For Anthos Config Management, it is recommended to use separate repositories for each environment, and use branches to separate the clusters within each environment. This way, you can enforce different policies and configurations for each environment, and use pull requests to promote changes across environments. You can also use Kustomize to create overlays for each cluster that apply specific patches or customizations34.
• For application code, it is recommended to use separate repositories for each application, and use branches to separate the features or bug fixes for each application. This way, you can isolate the development and testing of each application, and use pull requests to merge changes into the main branch. You can also use tags or labels to trigger deployments to different environments5 .

References:

1: Best practices for using Terraform | Google Cloud

2: Terraform Recommended Practices - Part 1 | Terraform - HashiCorp Learn

3: Deploy Anthos on GKE with Terraform part 1: GitOps with Config Sync | Google Cloud Blog

4: Using Kustomize with Anthos Config Management | Anthos Config Management Documentation | Google Cloud

5: Deploy Anthos on GKE with Terraform part 3: Continuous Delivery with Cloud Build | Google Cloud Blog

GitOps-style continuous delivery with Cloud Build | Cloud Build Documentation | Google Cloud

https://cloud.google.com/architecture/continuous-delivery-toolchain-spinnaker-cloud

https://spinnaker.io/guides/user/pipeline/triggers/pubsub/

 The most efficient way to build an automated pipeline that deploys the application when the image is updated is to use Cloud Pub/Sub to trigger a Spinnaker pipeline. This way, you can leverage the built-in integration between GCR and Cloud Pub/Sub, and use Spinnaker as a continuous delivery platform for deploying your application .

The Standard Tier network service offers lower network costs than the Premium Tier. This is the correct option to reduce the network cost for the application3.

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.

The first thing you should do to prepare your ecommerce application for the upcoming peak traffic season is to load test the application to profile its performance for scaling. Load testing is a process of simulating high traffic or user demand on your application and measuring how it responds. Load testing can help you identify any bottlenecks, errors, or performance issues that might affect your application during the busy season1. Load testing can also help you determine the optimal scaling strategy for your application, such as horizontal scaling (adding more instances) or vertical scaling (adding more resources to each instance)2.

There are different tools and methods for load testing your ecommerce application on Google Cloud, depending on the type and complexity of your application. For example, you can use Cloud Load Balancing to distribute traffic across multiple instances of your application, and use Cloud Monitoring to measure the latency, throughput, and error rate of your application3. You can also use Cloud Functions or Cloud Run to create serverless load generators that can simulate user requests and send them to your application4. Alternatively, you can use third-party tools such as Apache JMeter or Locust to create and run load tests on your application.

By load testing your ecommerce application before the peak traffic season, you can ensure that your application is ready to handle the expected load and provide a good user experience. You can also use the results of your load tests to plan and implement other steps to prepare your application for the busy season, such as migrating to a more scalable platform, creating a Terraform configuration for deploying to additional regions, or pre-provisioning additional compute power.

References:

1: Load Testing 101: How To Test Website Performance | BlazeMeter

2: Scaling applications | Google Cloud

3: Load testing using Google Cloud | Solutions | Google Cloud

4: Serverless load testing using Cloud Functions | Solutions | Google Cloud

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.

The best option for managing all charts uniformly, with native access control and VPC Service Controls is to store public and private charts in OCI format by using Artifact Registry. Artifact Registry is a service that allows you to store and manage container images and other artifacts in Google Cloud. Artifact Registry supports OCI format, which is an open standard for storing container images and other artifacts such as Helm charts. You can use Artifact Registry to store public and private charts in OCI format and manage them uniformly. You can also use Artifact Registry’s native access control features, such as IAM policies and VPC Service Controls, to secure your charts and control who can access them.

The best option for implementing the rollback actions is to update the Kubernetes Service to point to the previous Kubernetes Deployment. A Kubernetes Service is a resource that defines how to access a set of Pods. A Kubernetes Deployment is a resource that manages the creation and update of Pods. By using the blue/green deployment methodology, you can create two Deployments, one for the current version (blue) and one for the new version (green), and use a Service to switch traffic between them. If you need to rollback, you can update the Service to point to the previous Deployment (blue) and stop sending traffic to the new Deployment (green).

The best option for making the third-party application work while following Google-recommended security practices is to add a rule to set the iam.disableServiceAccountKeyCreation policy to off in your project and create a key. The iam.disableServiceAccountKeyCreation policy is an organization policy that controls whether service account keys can be created in a project or organization. By default, this policy is set to on, which means that service account keys cannot be created. However, you can override this policy at a lower level, such as a project, by adding a rule to set it to off. This way, you can create a service account key for your project without affecting other projects or organizations. You should also follow the best practices for managing service account keys, such as rotating them regularly, storing them securely, and deleting them when they are no longer needed.

The most cost-effective network tier and load balancing configuration for your frontend tier is to use Premium Tier with a regional load balancer. Premium Tier is a network tier that provides high-performance and low-latency network connectivity across Google’s global network. A regional load balancer is a load balancer that distributes traffic within a single region. Since your application is deployed entirely within the europe-west2 region and only serves users based in the United Kingdom, you can use Premium Tier with a regional load balancer to optimize the network performance and cost.

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.

https://cloud.google.com/billing/docs/how-to/export-data-bigquery

The correct answer is A. Add the Logs Writer role to the service account.

To use Cloud Logging, the service account attached to the Compute Engine instance must have the necessary permissions to write log entries. The Logs Writer role (roles/logging.logWriter) provides this permission. You can grant this role to the user-managed service account at the project, folder, or organization level1.

Private Google Access is not required for Cloud Logging, as it allows instances without external IP addresses to access Google APIs and services2. The default Compute Engine service account already has the Logs Writer role, but it is not a recommended practice to use it for user applications3. Exporting the service account key and configuring the agents to use the key is not a secure way of authenticating the service account, as it exposes the key to potential compromise4.

References:

1: Access control with IAM | Cloud Logging | Google Cloud

2: Private Google Access overview  |  VPC  |  Google Cloud

3: Service accounts  |  Compute Engine Documentation  |  Google Cloud

4: Best practices for securing service accounts  |  IAM Documentation  |  Google Cloud

The best option for ensuring that the three environments are consistent and following Google-recommended practices is to use Cloud Build to render and deploy the network policies and the DaemonSet, and set up Config Sync to sync the configurations for the three environments. Cloud Build is a service that executes your builds on Google Cloud infrastructure. You can use Cloud Build to render and deploy your network policies and DaemonSet as code using tools like Kustomize, Helm, or kpt. Config Sync is a feature that enables you to manage the configurations of your GKE clusters from a single source of truth, such as a Git repository. You can use Config Sync to sync the configurations for your development, staging, and production environments and ensure that they are consistent.

https://sre.google/sre-book/service-level-objectives/

The best option for configuring Cloud Monitoring to view the number of requests from a specific IP address with minimal operational overhead is to configure the Ops Agent with a logging receiver and create a logs-based metric. The Ops Agent is an agent that collects system metrics and logs from your VM instances and sends them to Cloud Monitoring and Cloud Logging. A logging receiver is a configuration that specifies which logs are collected by the Ops Agent and how they are processed. You can use a logging receiver to collect web server logs from your VM instances and send them to Cloud Logging. A logs-based metric is a metric that is extracted from log entries in Cloud Logging. You can use a logs-based metric to count the number of requests from a specific IP address by using a filter expression. You can then use Cloud Monitoring to view and analyze the logs-based metric.

https://cloud.google.com/kubernetes-engine/docs/concepts/custom-and-external-metrics#custom_metrics

https://github.com/GoogleCloudPlatform/k8s-stackdriver/blob/master/custom-metrics-stackdriver-adapter/README.md

Your application can report a custom metric to Cloud Monitoring. You can configure Kubernetes to respond to these metrics and scale your workload automatically. For example, you can scale your application based on metrics such as queries per second, writes per second, network performance, latency when communicating with a different application, or other metrics that make sense for your workload. https://cloud.google.com/kubernetes-engine/docs/concepts/custom-and-external-metrics

https://cloud.google.com/monitoring/api/metrics_gcp#gcp-appengine

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.