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Google Professional-Cloud-DevOps-Engineer certification is a comprehensive exam that evaluates the candidate's practical skills in the field of cloud computing and DevOps. Google Cloud Certified - Professional Cloud DevOps Engineer Exam certification is designed for professionals who have a good understanding of DevOps practices and tools, and can implement them in the GCP environment. Google Cloud Certified - Professional Cloud DevOps Engineer Exam certification is recognized globally and provides a competitive advantage to the candidates in the job market.
To earn the Google Professional-Cloud-DevOps-Engineer certification, you will need to pass a rigorous exam that covers a wide range of topics related to cloud computing, automation, and continuous delivery. Professional-Cloud-DevOps-Engineer exam consists of multiple-choice questions and is designed to evaluate your ability to design, implement, and manage cloud-based solutions using Google Cloud Platform. Professional-Cloud-DevOps-Engineer exam also tests your understanding of various DevOps tools and methodologies, such as Docker, Kubernetes, Ansible, and Jenkins. By passing Professional-Cloud-DevOps-Engineer exam, you will demonstrate your proficiency in using Google Cloud Platform to build and deploy scalable and reliable applications and gain recognition as a certified professional in cloud DevOps engineering.
NEW QUESTION # 65
You are running an application in a virtual machine (VM) using a custom Debian image. The image has the Stackdriver Logging agent installed. The VM has the cloud-platform scope. The application is logging information via syslog. You want to use Stackdriver Logging in the Google Cloud Platform Console to visualize the logs. You notice that syslog is not showing up in the "All logs" dropdown list of the Logs Viewer. What is the first thing you should do?
- A. Install the most recent version of the Stackdriver agent.
- B. Verify the VM service account access scope includes the monitoring.write scope.
- C. SSH to the VM and execute the following commands on your VM: ps ax I grep fluentd
- D. Look for the agent's test log entry in the Logs Viewer.
Answer: C
Explanation:
Explanation
https://cloud.google.com/compute/docs/access/service-accounts#associating_a_service_account_to_an_instance
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 Ofyour applications. You need to test against the full production load before your applications are launched. What should you do?
- A. Use canary testing with continuous deployment.
- B. Use A/B testing with blue/green deployment.
- C. Use shadow testing with continuous deployment.
- D. Use canary testing with rolling updates deployment,
Answer: C
NEW QUESTION # 67
Your company recently migrated to Google Cloud. You need to design a fast, reliable, and repeatable solution for your company to provision new projects and basic resources in Google Cloud. What should you do?
- A. Use the Google Cloud console to create projects.
- B. Write a Terraform module and save it in your source control repository. Copy and run the apply command to create the new project.
- C. Use the Terraform repositories from the Cloud Foundation Toolkit. Apply the code with appropriate parameters to create the Google Cloud project and related resources.
- D. Write a script by using the gcloud CLI that passes the appropriate parameters from the request. Save the script in a Git repository.
Answer: C
Explanation:
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.
NEW QUESTION # 68
You are configuring connectivity across Google Kubernetes Engine (GKE) clusters in different VPCs You notice that the nodes in Cluster A are unable to access the nodes in Cluster B You suspect that the workload access issue is due to the network configuration You need to troubleshoot the issue but do not have execute access to workloads and nodes You want to identify the layer at which the network connectivity is broken What should you do?
- A. Use Network Connectivity Center to perform a Connectivity Test from Cluster A to Cluster
- B. Enable VPC Flow Logs in both VPCs and monitor packet drops
- C. Use a debug container to run the traceroute command from Cluster A to Cluster B and from Cluster B to Cluster A Identify the common failure point
- D. Install a toolbox container on the node in Cluster A Confirm that the routes to Cluster B are configured appropriately
Answer: A
Explanation:
Explanation
The best option for troubleshooting the issue without having execute access to workloads and nodes is to use Network Connectivity Center to perform a Connectivity Test from Cluster A to Cluster B. Network Connectivity Center is a service that allows you to create, manage, and monitor network connectivity across Google Cloud, hybrid, and multi-cloud environments. You can use Network Connectivity Center to perform a Connectivity Test, which is a feature that allows you to test the reachability and latency between two endpoints, such as GKE clusters, VM instances, or IP addresses. By using Network Connectivity Center to perform a Connectivity Test from Cluster A to Cluster B, you can identify the layer at which the network connectivity is broken, such as the firewall, routing, or load balancing.
NEW QUESTION # 69
Your team of Infrastructure DevOps Engineers is growing, and you are starting to use Terraform to manage infrastructure. You need a way to implement code versioning and to share code with other team members. What should you do?
- A. Store the Terraform code in a network shared folder with child folders for each version release. Ensure that everyone works on different files.
- B. Store the Terraform code in a Cloud Storage bucket using object versioning. Give access to the bucket to every team member so they can download the files.
- C. Store the Terraform code in a shared Google Drive folder so it syncs automatically to every team member's computer. Organize files with a naming convention that identifies each new version.
- D. Store the Terraform code in a version-control system. Establish procedures for pushing new versions and merging with the master.
Answer: D
Explanation:
https://www.terraform.io/docs/cloud/guides/recommended-practices/part3.3.html
NEW QUESTION # 70
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. Get the product team to prioritize reliability work over new features.
- C. Increase the service's deployment velocity and/or risk.
- D. Make the service's SLO more strict.
- E. Change the implementation of your Service Level Indicators (SLIs) to increase coverage.
Answer: A,C
Explanation:
Explanation
(https://sre.google/workbook/implementing-slos/#slo-decision-matrix)
NEW QUESTION # 71
You are reviewing your deployment pipeline in Google Cloud Deploy You must reduce toil in the pipeline and you want to minimize the amount of time it takes to complete an end-to-end deployment What should you do?
Choose 2 answers
- A. Automate promotion approvals from the development environment to the test environment
- B. Divide the automation steps into smaller tasks
- C. Add more engineers to finish the manual steps.
- D. Use a script to automate the creation of the deployment pipeline in Google Cloud Deploy
- E. Create a trigger to notify the required team to complete the next step when manual intervention is required
Answer: A,E
Explanation:
Explanation
The best options for reducing toil in the pipeline and minimizing the amount of time it takes to complete an end-to-end deployment are to create a trigger to notify the required team to complete the next step when manual intervention is required and to automate promotion approvals from the development environment to the test environment. A trigger is a resource that initiates a deployment when an event occurs, such as a code change, a schedule, or a manual request. You can create a trigger to notify the required team to complete the next step when manual intervention is required by using Cloud Build or Cloud Functions. This way, you can reduce the waiting time and human errors in the pipeline. A promotion approval is a process that allows you to approve or reject a deployment from one environment to another, such as from development to test. You can automate promotion approvals from the development environment to the test environment by using Google Cloud Deploy or Cloud Build. This way, you can speed up the deployment process and avoid manual steps.
NEW QUESTION # 72
You are the Site Reliability Engineer responsible for managing your company's data services and products. You regularly navigate operational challenges, such as unpredictable data volume and high cost, with your company's data ingestion processes. You recently learned that a new data ingestion product will be developed in Google Cloud. You need to collaborate with the product development team to provide operational input on the new product. What should you do?
- A. When the initial product version passes the quality assurance phase and compliance assessments, deploy the product to a staging environment. Share error logs and performancemetrics with the product development team.
- B. Deploy the prototype product in a test environment, run a load test, and share the results with the product development team.
- C. When the new product is used by at least one internal customer in production, share error logs and monitoring metrics with the product development team.
- D. Review the design of the product with the product development team to provide feedback early in the design phase.
Answer: D
NEW QUESTION # 73
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. Add logic to the deployment pipeline to check that all manifests contain only images from gcr.io/altostrat-images.
- B. Add a tag to each image in gcr.io/altostrat-images and check that this tag is present when the image is deployed.
- C. Use a Binary Authorization policy that includes the whitelist name pattern gcr.io/attostrat-images/.
- D. Create a custom builder for Cloud Build that will only push images to gcr.io/altostrat-images.
Answer: D
NEW QUESTION # 74
You are investigating issues in your production application that runs on Google Kubernetes Engine (GKE).
You determined that the source Of the issue is a recently updated container image, although the exact change in code was not identified. The deployment is currently pointing to the latest tag. You need to update your cluster to run a version of the container that functions as intended. What should you do?
- A. Apply the latest tag to the previous container image, and do a rolling update on the deployment.
- B. Create a new tag called stable that points to the previously working container, and change the deployment to point to the new tag.
- C. Build a new container from a previous Git tag, and do a rolling update on the deployment to the new container.
- D. Alter the deployment to point to the sha2 56 digest of the previously working container.
Answer: D
NEW QUESTION # 75
Your company has a Google Cloud resource hierarchy with folders for production test and development Your cyber security team needs to review your company's Google Cloud security posture to accelerate security issue identification and resolution You need to centralize the logs generated by Google Cloud services from all projects only inside your production folder to allow for alerting and near-real time analysis. What should you do?
- A. Create an aggregated log sink associated with the production folder that uses a Cloud Logging bucket as the destination
- B. Create a central Cloud Monitoring workspace and attach all related projects
- C. Enable the Workflows API and route all the logs to Cloud Logging
- D. Create an aggregated log sink associated with the production folder that uses a Pub Sub topic as the destination
Answer: A
Explanation:
Explanation
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.
NEW QUESTION # 76
Your company is creating a new cloud-native Google Cloud organization. You expect this Google Cloud organization to first be used by a small number of departments and then expand to be used by a large number of departments. Each department has a large number of applications varying in size. You need to design the VPC network architecture. Your solution must minimize the amount of management required while remaining flexible enough for development teams to quickly adapt to their evolving needs. What should you do?
- A. Create a separate VPC for each department and connect the VPCs with VPC Network Peering.
- B. Create a separate VPC for each department and connect the VPCs with Cloud VPN.
- C. Create a separate VPC for each application and use Private Service Connect to connect the VPCs.
- D. Create a separate VPC for each department and use Private Service Connect to connect the VPCs.
Answer: A
NEW QUESTION # 77
Your company runs applications in Google Kubernetes Engine (GKE) that are deployed following a GitOps methodology.
Application developers frequently create cloud resources to support their applications. You want to give developers the ability to manage infrastructure as code, while ensuring that you follow Google-recommended practices. You need to ensure that infrastructure as code reconciles periodically to avoid configuration drift.
What should you do?
- A. Create a Pod resource with a Terraform docker image to execute terraform plan and terraform apply commands.
- B. Install and configure Config Connector in Google Kubernetes Engine (GKE).
- C. Create a Job resource with a Terraform docker image to execute terraforrm plan and terraform apply commands.
- D. Configure Cloud Build with a Terraform builder to execute plan and apply commands.
Answer: B
Explanation:
Explanation
The best option to give developers the ability to manage infrastructure as code, while ensuring that you follow Google-recommended practices, is to install and configure Config Connector in Google Kubernetes Engine (GKE).
Config Connector is a Kubernetes add-on that allows you to manage Google Cloud resources through Kubernetes. You can use Config Connector to create, update, and delete Google Cloud resources using Kubernetes manifests. Config Connector also reconciles the state of the Google Cloud resources with the desired state defined in the manifests, ensuring that there is no configuration drift1.
Config Connector follows the GitOps methodology, as it allows you to store your infrastructure configuration in a Git repository, and use tools such as Anthos Config Management or Cloud Source Repositories to sync the configuration to your GKE cluster. This way, you can use Git as the source of truth for your infrastructure, and enable reviewable and version-controlled workflows2.
Config Connector can be installed and configured in GKE using either the Google Cloud Console or the gcloud command-line tool. You need to enable the Config Connector add-on for your GKE cluster, and create a Google Cloud service account with the necessary permissions to manage the Google Cloud resources. You also need to create a Kubernetes namespace for each Google Cloud project that you want to manage with Config Connector3.
By using Config Connector in GKE, you can give developers the ability to manage infrastructure as code, while ensuring that you follow Google-recommended practices. You can also benefit from the features and advantages of Kubernetes, such as declarative configuration, observability, and portability4.
References:
1: Overview | Artifact Registry Documentation | Google Cloud
2: Deploy Anthos on GKE with Terraform part 1: GitOps with Config Sync | Google Cloud Blog
3: Installing Config Connector | Config Connector Documentation | Google Cloud
4: Why use Config Connector? | Config Connector Documentation | Google Cloud
NEW QUESTION # 78
You are the Site Reliability Engineer responsible for managing your company's data services and products.
You regularly navigate operational challenges, such as unpredictable data volume and high cost, with your company's data ingestion processes. You recently learned that a new data ingestion product will be developed in Google Cloud. You need to collaborate with the product development team to provide operational input on the new product. What should you do?
- A. Deploy the prototype product in a test environment, run a load test, and share the results with the product development team.
- B. When the new product is used by at least one internal customer in production, share error logs and monitoring metrics with the product development team.
- C. When the initial product version passes the quality assurance phase and compliance assessments, deploy the product to a staging environment. Share error logs and performance metrics with the product development team.
- D. Review the design of the product with the product development team to provide feedback early in the design phase.
Answer: D
Explanation:
Explanation
The correct answer is D. Review the design of the product with the product development team to provide feedback early in the design phase.
According to the Google Cloud DevOps best practices, a Site Reliability Engineer (SRE) should collaborate with the product development team from the beginning of the product lifecycle, not just after the product is deployed or tested. This way, the SRE can provide operational input on the product design, such as scalability, reliability, security, and cost efficiency. The SRE can also help define service level objectives (SLOs) and service level indicators (SLIs) for the product, as well as monitoring and alerting strategies. By collaborating early and often, the SRE and the product development team can ensure that the product meets the operational requirements and expectations of the customers.
NEW QUESTION # 79
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 applicationruns 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. Add high memory compute nodes to the cluster.
- B. Increase the CPU limit in the application deployment.
- C. Add Cloud Trace to the application, and redeploy.
- D. Increase the memory limit in the application deployment.
Answer: D
NEW QUESTION # 80
Your company processes IOT data at scale by using Pub/Sub, App Engine standard environment, and an application written in GO. You noticed that the performance inconsistently degrades at peak load. You could not reproduce this issue on your workstation. You need to continuously monitor the application in production to identify slow paths in the code. You want to minimize performance impact and management overhead.
What should you do?
- A. Use Cloud Monitoring to assess the App Engine CPU utilization metric.
- B. Periodically run the go tool pprof command against the application instance. Analyze the results by using flame graphs.
- C. Configure Cloud Profiler, and initialize the [email protected]/go/profiler library in the application.
- D. Install a continuous profiling tool into Compute Engine. Configure the application to send profiling data to the tool.
Answer: C
Explanation:
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.
Reference:
Cloud Profiler documentation, Overview. Profiling Go applications, Supported environments. Profiling Go applications, Using Cloud Profiler. Analyzing data, Analyzing data.
NEW QUESTION # 81
You are running a web application that connects to an AlloyDB cluster by using a private IP address in your default VPC. You need to run a database schema migration in your CI/CD pipeline by using Cloud Build before deploying a new version of your application. You want to follow Google-recommended security practices. What should you do?
- A. Create a service account that has permission to access the database. Configure Cloud Build to use this service account and execute the schema migration script in a private pool.
- B. Set up a Cloud Build private pool to access the database through a static external IP address. Configure the database to only allow connections from this IP address. Execute the schema migration script in the private pool.
- C. Add the database username and password to Secret Manager. When running the schema migration script, retrieve the username and password from Secret Manager.
- D. Add the database username and encrypted password to the application configuration file. Use these credentials in Cloud Build to execute the schema migration script.
Answer: A
Explanation:
To securely connect Cloud Build to an AlloyDB cluster using a private IP address and adhere to Google- recommended security practices, you need to address two main aspects:
Network Connectivity:Ensuring Cloud Build can reach the private IP of the AlloyDB cluster.
Authentication/Credential Management:Securely authenticating Cloud Build to the AlloyDB cluster.
Let's break down why Option B is the most suitable:
Cloud Build Private Pool:AlloyDB is accessed via a private IP in your VPC. Cloud Build's default build environment runs on Google-managed infrastructure outside your VPC and cannot directly access private IP addresses. To enable this, you must use aCloud Build private pool. A private pool can be configured with VPC peering to your default VPC, allowing build steps running within that pool to access resources like your AlloyDB cluster via their private IPs. Option B correctly includes "execute the schema migration script in a private pool." Service Account with Permissions (IAM Database Authentication):AlloyDB supports IAM database authentication. This is a Google-recommended security practice because it allows you to manage database access using Google Cloud's Identity and Access Management (IAM) rather than relying on traditional database passwords.
You would create a dedicated service account for Cloud Build (or use the private pool's service account).
This service account would be granted the necessary IAM roles to connect to the AlloyDB instance (e.g., roles
/alloydb.client) and a database-level IAM role for login (e.g., roles/alloydb.user or roles/alloydb.admin depending on the permissions needed for schema migration).
Cloud Build would then be configured to use this service account. The "permission to access the database" in Option B refers to these IAM permissions. This method avoids managing and distributing database passwords.
Analyzing the options:
A: Set up a Cloud Build private pool to access the database through a static external IP address...
While using a private pool is correct for network access, routing this through a staticexternalIP for a resource that has aprivateIP is generally not the first-choice secure pattern if direct private access is feasible. It adds complexity and a potential external exposure point, even if firewalled. The aim is to keep traffic within the private network as much as possible.
B: Create a service account that has permission to access the database. Configure Cloud Build to use this service account and execute the schema migration script in a private pool.
This option correctly combines the use of aprivate pool(for private IP network access) with aservice account having permissions(strongly implying IAM database authentication for AlloyDB, which is a best practice).
This is a secure and robust approach.
C: Add the database username and encrypted password to the application configuration file...
Storing credentials, even if "encrypted" (the method and key management for encryption are unspecified and problematic), in application configuration files checked into source control or packaged with the application is a significant security risk and not a recommended practice.
D: Add the database username and password to Secret Manager. When running the schema migration script, retrieve the username and password from Secret Manager.
UsingSecret Managerto store database usernames and passwords is a Google-recommended practiceifyou are using password-based authentication. However, this optionalonedoes not solve the network connectivity issue for Cloud Build to reach the private IP of AlloyDB. You would still need a private pool. While D is good for secret management, B offers a more comprehensive solution that includes both the network aspect and implies a more modern authentication method (IAM database auth). If the question forced a choice between only doing secure credential storage (D) or doing IAM auth + private networking (B), B is more complete for the overall task.
Conclusion:Option B is the most aligned with Google-recommended security practices as it addresses both the necessary private network connectivity via a Cloud Build private pool and promotes the use of IAM-based database authentication for AlloyDB, which is generally preferred over managing passwords.
References (General Concepts):
Cloud Build Private Pools for VPC Access:Google Cloud documentation for Cloud Build explicitly details using private pools to connect to resources in a VPC network.
See:https://www.google.com/search?q=https://cloud.google.com/build/docs/private-pools/accessing-private- resources-with-private-pools AlloyDB IAM Database Authentication:Google Cloud documentation for AlloyDB highlights IAM database authentication as a secure method.
See:https://www.google.com/search?q=https://cloud.google.com/alloydb/docs/iam-authentication Secret Manager:If password authentication were the only option, Secret Manager would be the recommended way to store those credentials.
See:https://cloud.google.com/secret-manager
Option B synergizes the benefits of private networking and modern IAM-based authentication for a comprehensive secure solution.
NEW QUESTION # 82
You are developing the deployment and testing strategies for your CI/CD pipeline in Google Cloud You must be able to
* Reduce the complexity of release deployments and minimize the duration of deployment rollbacks
* Test real production traffic with a gradual increase in the number of affected users You want to select a deployment and testing strategy that meets your requirements What should you do?
- A. Rolling update deployment and shadow testing
- B. Recreate deployment and canary testing
- C. Blue/green deployment and canary testing
- D. Rolling update deployment and A/B testing
Answer: C
Explanation:
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.
NEW QUESTION # 83
Your organization wants to increase the availability target of an application from 99 9% to 99 99% for an investment of $2 000 The application's current revenue is S1,000,000 You need to determine whether the increase in availability is worth the investment for a single year of usage What should you do?
- A. Calculate the value of improved availability to be $1 000 and determine that the increase in availability is worth the investment
- B. Calculate the value of improved availability to be $900, and determine that the increase in availability is not worth the investment
- C. Calculate the value of improved availability to be $9,000. and determine that the increase in availability is worth the investment
- D. Calculate the value of improved availability to be $1 000 and determine that the increase in availability is not worth the investment
Answer: B
NEW QUESTION # 84
Your application images are built and pushed to Google Container Registry (GCR). You want to build an automated pipeline that deploys the application when the image is updated while minimizing the development effort. What should you do?
- A. Use Cloud Pub/Sub to trigger a Spinnaker pipeline.
- B. Use Cloud Build to trigger a Spinnaker pipeline.
- C. Use Cloud Pub/Sub to trigger a custom deployment service running in Google Kubernetes Engine (GKE).
- D. Use a custom builder in Cloud Build to trigger a Jenkins pipeline.
Answer: C
NEW QUESTION # 85
You use Spinnaker to deploy your application and have created a canary deployment stage in the pipeline.
Your application has an in-memory cache that loads objects at start time. You want to automate the comparison of the canary version against the production version. How should you configure the canary analysis?
- A. Compare the canary with a new deployment of the previous production version.
- B. Compare the canary with the average performance of a sliding window of previous production versions.
- C. Compare the canary with the existing deployment of the current production version.
- D. Compare the canary with a new deployment of the current production version.
Answer: D
Explanation:
Explanation
https://cloud.google.com/architecture/automated-canary-analysis-kubernetes-engine-spinnaker
https://spinnaker.io/guides/user/canary/best-practices/#compare-canary-against-baseline-not-against-production
NEW QUESTION # 86
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