You are developing a microservice-based application that will be deployed on a Google Kubernetes Engine cluster. The application needs to read and write to a Spanner database. You want to follow security best practices while minimizing code changes. How should you configure your application to retrieve Spanner credentials?

Configure the appropriate service accounts, and use Workload Identity to run the pods.

Store the application credentials as Kubernetes Secrets, and expose them as environment variables.

Configure the appropriate routing rules, and use a VPC-native cluster to directly connect to the database.

Store the application credentials using Cloud Key Management Service, and retrieve them whenever a database connection is made.

You are deploying your application on a Compute Engine instance that communicates with Cloud SQL. You will use Cloud SQL Proxy to allow your application to communicate to the database using the service account associated with the application's instance. You want to follow the Google-recommended best practice of providing minimum access for the role assigned to the service account. What should you do?

Assign the Cloud SQL Client role.

Assign the Project Editor role.

Assign the Cloud SQL Client role.

Assign the Cloud SQL Editor role.

Your team develops stateless services that run on Google Kubernetes Engine (GKE). You need to deploy a new service that will only be accessed by other services running in the GKE cluster. The service will need to scale as quickly as possible to respond to changing load. What should you do?

Use a Horizontal Pod Autoscaler to scale the containers, and expose them via a ClusterIP Service.

Use a Vertical Pod Autoscaler to scale the containers, and expose them via a ClusterIP Service.

Use a Vertical Pod Autoscaler to scale the containers, and expose them via a NodePort Service.

Use a Horizontal Pod Autoscaler to scale the containers, and expose them via a ClusterIP Service.

Use a Horizontal Pod Autoscaler to scale the containers, and expose them via a NodePort Service.

You recently migrated a monolithic application to Google Cloud by breaking it down into microservices. One of the microservices is deployed using Cloud Functions. As you modernize the application, you make a change to the API of the service that is backward-incompatible. You need to support both existing callers who use the original API and new callers who use the new API. What should you do?

Re-deploy the Cloud Function after making code changes to support the new API. Requests for both versions of the API are fulfilled based on a version identifier included in the call.

Leave the original Cloud Function as-is and deploy a second Cloud Function with the new API. Use a load balancer to distribute calls between the versions.

Leave the original Cloud Function as-is and deploy a second Cloud Function that includes only the changed API. Calls are automatically routed to the correct function.

Leave the original Cloud Function as-is and deploy a second Cloud Function with the new API. Use Cloud Endpoints to provide an API gateway that exposes a versioned API.

Re-deploy the Cloud Function after making code changes to support the new API. Requests for both versions of the API are fulfilled based on a version identifier included in the call.

Your company just experienced a Google Kubernetes Engine (GKE) API outage due to a zone failure. You want to deploy a highly available GKE architecture that minimizes service interruption to users in the event of a future zone failure. What should you do?

Deploy GKE on-premises clusters

Your team develops services that run on Google Cloud. You want to process messages sent to a Pub/Sub topic, and then store them. Each message must be processed exactly once to avoid duplication of data and any data conflicts. You need to use the cheapest and most simple solution. What should you do?

Process the messages with a Dataflow streaming pipeline using Apache Beam's PubSubIO package, and write the output to storage.

Process the messages with a Dataproc job, and write the output to storage.

Process the messages with a Dataflow streaming pipeline using Apache Beam's PubSubIO package, and write the output to storage.

Process the messages with a Cloud Function, and write the results to a BigQuery location where you can run a job to deduplicate the data.

Retrieve the messages with a Dataflow streaming pipeline, store them in Cloud Bigtable, and use another Dataflow streaming pipeline to deduplicate messages.

You are running a containerized application on Google Kubernetes Engine. Your container images are stored in Container Registry. Your team uses CI/CD practices. You need to prevent the deployment of containers with known critical vulnerabilities. What should you do?

* Enable the Container Scanning API to perform vulnerability scanning * Programmatically review vulnerability reporting through the Container Scanning API, and provide an attestation that the container is free of known critical vulnerabilities * Use Binary Authorization to implement a policy that forces the attestation to be provided before the container is deployed

* Use Web Security Scanner to automatically crawl your application * Review your application logs for scan results, and provide an attestation that the container is free of known critical vulnerabilities * Use Binary Authorization to implement a policy that forces the attestation to be provided before the container is deployed

* Use Web Security Scanner to automatically crawl your application * Review the scan results in the scan details page in the Cloud Console, and provide an attestation that the container is free of known critical vulnerabilities * Use Binary Authorization to implement a policy that forces the attestation to be provided before the container is deployed

* Enable the Container Scanning API to perform vulnerability scanning * Review vulnerability reporting in Container Registry in the Cloud Console, and provide an attestation that the container is free of known critical vulnerabilities * Use Binary Authorization to implement a policy that forces the attestation to be provided before the container is deployed

* Enable the Container Scanning API to perform vulnerability scanning * Programmatically review vulnerability reporting through the Container Scanning API, and provide an attestation that the container is free of known critical vulnerabilities * Use Binary Authorization to implement a policy that forces the attestation to be provided before the container is deployed

You have an on-premises application that authenticates to the Cloud Storage API using a user-managed service account with a user-managed key. The application connects to Cloud Storage using Private Google Access over a Dedicated Interconnect link. You discover that requests from the application to access objects in the Cloud Storage bucket are failing with a 403 Permission Denied error code. What is the likely cause of this issue?

The service account key has been rotated but not updated on the application server.

The folder structure inside the bucket and object paths have changed.

The permissions of the service account's predefined role have changed.

The service account key has been rotated but not updated on the application server.

The Interconnect link from the on-premises data center to Google Cloud is experiencing a temporary outage.

You are using the Cloud Client Library to upload an image in your application to Cloud Storage. Users of the application report that occasionally the upload does not complete and the client library reports an HTTP 504 Gateway Timeout error. You want to make the application more resilient to errors. What changes to the application should you make?

Write an exponential backoff process around the client library call.

Write a one-second wait time backoff process around the client library call.

Design a retry button in the application and ask users to click if the error occurs.

Create a queue for the object and inform the users that the application will try again in 10 minutes.

You are building a mobile application that will store hierarchical data structures in a database. The application will enable users working offline to sync changes when they are back online. A backend service will enrich the data in the database using a service account. The application is expected to be very popular and needs to scale seamlessly and securely. Which database and IAM role should you use?

Use Firestore in Native mode and assign the roles/datastore.user role to the service account.

Use Cloud SQL, and assign the roles/cloudsql.editor role to the service account.

Use Bigtable, and assign the roles/bigtable.viewer role to the service account.

Use Firestore in Native mode and assign the roles/datastore.user role to the service account.

Use Firestore in Datastore mode and assign the roles/datastore.viewer role to the service account.

Your application is deployed on hundreds of Compute Engine instances in a managed instance group (MIG) in multiple zones. You need to deploy a new instance template to fix a critical vulnerability immediately but must avoid impact to your service. What setting should be made to the MIG after updating the instance template?

Set the Update mode to Opportunistic.

Set the Maximum Unavailable to 100%.

Set the Minimum Wait time to 0 seconds.

You made a typo in a low-level Linux configuration file that prevents your Compute Engine instance from booting to a normal run level. You just created the Compute Engine instance today and have done no other maintenance on it, other than tweaking files. How should you correct this error?

Configure and log in to the Compute Engine instance through the serial port, and change the file

Download the file using scp, change the file, and then upload the modified version

Configure and log in to the Compute Engine instance through SSH, and change the file

Configure and log in to the Compute Engine instance through the serial port, and change the file

Configure and log in to the Compute Engine instance using a remote desktop client, and change the file

You are developing an application that needs to store files belonging to users in Cloud Storage. You want each user to have their own subdirectory in Cloud Storage. When a new user is created, the corresponding empty subdirectory should also be created. What should you do?

Create an object with the name of the subdirectory ending with a trailing slash ('/') that is zero bytes in length.

Create an object with the name of the subdirectory, and then immediately delete the object within that subdirectory.

Create an object with the name of the subdirectory that is zero bytes in length and has WRITER access control list permission.

Create an object with the name of the subdirectory that is zero bytes in length. Set the Content-Type metadata to CLOUDSTORAGE_FOLDER.

Your company's corporate policy states that there must be a copyright comment at the very beginning of all source files. You want to write a custom step in Cloud Build that is triggered by each source commit. You need the trigger to validate that the source contains a copyright and add one for subsequent steps if not there. What should you do?

Build a new Docker container that examines the files in /workspace and then checks and adds a copyright for each source file. Changed files are explicitly committed back to the source repository.

Build a new Docker container that examines the files in /workspace and then checks and adds a copyright for each source file. Changed files do not need to be committed back to the source repository.

Build a new Docker container that examines the files in a Cloud Storage bucket and then checks and adds a copyright for each source file. Changed files are written back to the Cloud Storage bucket.

Build a new Docker container that examines the files in a Cloud Storage bucket and then checks and adds a copyright for each source file. Changed files are explicitly committed back to the source repository.

One of your deployed applications in Google Kubernetes Engine (GKE) is having intermittent performance issues. Your team uses a third-party logging solution. You want to install this solution on each node in your GKE cluster so you can view the logs. What should you do?

Deploy the third-party solution as a DaemonSet

Modify your container image to include the monitoring software

Use SSH to connect to the GKE node, and install the software manually

Deploy the third-party solution using Terraform and deploy the logging Pod as a Kubernetes Deployment

You are in the final stage of migrating an on-premises data center to Google Cloud. You are quickly approaching your deadline, and discover that a web API is running on a server slated for decommissioning. You need to recommend a solution to modernize this API while migrating to Google Cloud. The modernized web API must meet the following requirements:* Autoscales during high traffic periods at the end of each month* Written in Python 3.x* Developers must be able to rapidly deploy new versions in response to frequent code changesYou want to minimize cost, effort, and operational overhead of this migration. What should you do?

Modernize and deploy the code on App Engine standard environment.

Modernize and deploy the code on App Engine flexible environment.

Modernize and deploy the code on App Engine standard environment.

Deploy the modernized application to an n1-standard-1 Compute Engine instance.

Ask the development team to re-write the application to run as a Docker container on Google Kubernetes Engine.

You are developing an application that will allow users to read and post comments on news articles. You want to configure your application to store and display user-submitted comments using Firestore. How should you design the schema to support an unknown number of comments and articles?

Store each comment in a document, and add the comment's key to an array property on the user profile.

Store each comment in a subcollection of the article.

Add each comment to an array property on the article.

Store each comment in a document, and add the comment's key to an array property on the article.

Store each comment in a document, and add the comment's key to an array property on the user profile.

You are a developer working with the CI/CD team to troubleshoot a new feature that your team introduced. The CI/CD team used HashiCorp Packer to create a new Compute Engine image from your development branch. The image was successfully built, but is not booting up. You need to investigate the issue with the CI/CD team. What should you do?

Check Compute Engine OS logs using the serial port, and check the Cloud Logging logs to confirm access to the serial port.

Create a new feature branch, and ask the build team to rebuild the image.

Shut down the deployed virtual machine, export the disk, and then mount the disk locally to access the boot logs.

Install Packer locally, build the Compute Engine image locally, and then run it in your personal Google Cloud project.

Check Compute Engine OS logs using the serial port, and check the Cloud Logging logs to confirm access to the serial port.

You manage an application that runs in a Compute Engine instance. You also have multiple backend services executing in stand-alone Docker containers running in Compute Engine instances. The Compute Engine instances supporting the backend services are scaled by managed instance groups in multiple regions. You want your calling application to be loosely coupled. You need to be able to invoke distinct service implementations that are chosen based on the value of an HTTP header found in the request. Which Google Cloud feature should you use to invoke the backend services?

Internal HTTP(S) Load Balancing

Your team is developing an ecommerce platform for your company. Users will log in to the website and add items to their shopping cart. Users will be automatically logged out after 30minutes of inactivity. When users log back in, their shopping cart should be saved. How should you store users' session and shopping cart information while following Google-recommended best practices?

Store the session information in Memorystore for Redis or Memorystore for Memcached, and store the shopping cart information in Firestore.

Store the session information in Pub/Sub, and store the shopping cart information in Cloud SQL.

Store the shopping cart information in a file on Cloud Storage where the filename is the SESSION ID.

Store the session and shopping cart information in a MySQL database running on multiple Compute Engine instances.

Store the session information in Memorystore for Redis or Memorystore for Memcached, and store the shopping cart information in Firestore.

You are designing a resource-sharing policy for applications used by different teams in a Google Kubernetes Engine cluster. You need to ensure that all applications can access the resources needed to run. What should you do? (Choose two.)

Create a namespace for each team, and attach resource quotas to each namespace.

Create a LimitRange to specify the default compute resource requirements for each namespace.

Specify the resource limits and requests in the object specifications.

Create a namespace for each team, and attach resource quotas to each namespace.

Create a LimitRange to specify the default compute resource requirements for each namespace.

Create a Kubernetes service account (KSA) for each application, and assign each KSA to the namespace.

Use the Anthos Policy Controller to enforce label annotations on all namespaces. Use taints and tolerations to allow resource sharing for namespaces.

You are developing a new application that has the following design requirements:Creation and changes to the application infrastructure are versioned and auditable.The application and deployment infrastructure uses Google-managed services as much as possible.The application runs on a serverless compute platform.How should you design the application's architecture?

1. Deploy the application infrastructure using gcloud commands. 2. Use Cloud Build to define a continuous integration pipeline for changes to the application source code. 3. Configure a pipeline step to pull the application source code from a Git repository, and create a containerized application. 4. Deploy the new container on Cloud Run as a pipeline step.

1. Store the application and infrastructure source code in a Git repository. 2. Use Cloud Build to deploy the application infrastructure with Terraform. 3. Deploy the application to a Cloud Function as a pipeline step.

1. Deploy Jenkins from the Google Cloud Marketplace, and define a continuous integration pipeline in Jenkins. 2. Configure a pipeline step to pull the application source code from a Git repository. 3. Deploy the application source code to App Engine as a pipeline step.

1. Create a continuous integration pipeline on Cloud Build, and configure the pipeline to deploy the application infrastructure using Deployment Manager templates. 2. Configure a pipeline step to create a container with the latest application source code. 3. Deploy the container to a Compute Engine instance as a pipeline step.

1. Deploy the application infrastructure using gcloud commands. 2. Use Cloud Build to define a continuous integration pipeline for changes to the application source code. 3. Configure a pipeline step to pull the application source code from a Git repository, and create a containerized application. 4. Deploy the new container on Cloud Run as a pipeline step.

You are creating and running containers across different projects in Google Cloud. The application you are developing needs to access Google Cloud services from within Google Kubernetes Engine (GKE).What should you do?

Use a Google service account to run the Pod with Workload Identity.

Assign a Google service account to the GKE nodes.

Use a Google service account to run the Pod with Workload Identity.

Store the Google service account credentials as a Kubernetes Secret.

Use a Google service account with GKE role-based access control (RBAC).

You have containerized a legacy application that stores its configuration on an NFS share. You need to deploy this application to Google Kubernetes Engine (GKE) and do not want the application serving traffic until after the configuration has been retrieved. What should you do?

Create a PersistentVolumeClaim on the GKE cluster. Access the configuration files from the volume, and start the service using an ENTRYPOINT script.

Use the gsutil utility to copy files from within the Docker container at startup, and start the service using an ENTRYPOINT script.

Create a PersistentVolumeClaim on the GKE cluster. Access the configuration files from the volume, and start the service using an ENTRYPOINT script.

Use the COPY statement in the Dockerfile to load the configuration into the container image. Verify that the configuration is available, and start the service using an ENTRYPOINT script.

Add a startup script to the GKE instance group to mount the NFS share at node startup. Copy the configuration files into the container, and start the service using an ENTRYPOINT script.

Your team is developing a new application using a PostgreSQL database and Cloud Run. You are responsible for ensuring that all traffic is kept private on Google Cloud. You want to use managed services and follow Google-recommended best practices. What should you do?

1. Enable Cloud SQL and Cloud Run in the same project. 2. Configure a private IP address for Cloud SQL. Enable private services access. 3. Create a Serverless VPC Access connector. 4. Configure Cloud Run to use the connector to connect to Cloud SQL.

1. Install PostgreSQL on a Compute Engine virtual machine (VM), and enable Cloud Run in the same project. 2. Configure a private IP address for the VM. Enable private services access. 3. Create a Serverless VPC Access connector. 4. Configure Cloud Run to use the connector to connect to the VM hosting PostgreSQL.

1. Use Cloud SQL and Cloud Run in different projects. 2. Configure a private IP address for Cloud SQL. Enable private services access. 3. Create a Serverless VPC Access connector. 4. Set up a VPN connection between the two projects. Configure Cloud Run to use the connector to connect to Cloud SQL.

1. Install PostgreSQL on a Compute Engine VM, and enable Cloud Run in different projects. 2. Configure a private IP address for the VM. Enable private services access. 3. Create a Serverless VPC Access connector. 4. Set up a VPN connection between the two projects. Configure Cloud Run to use the connector to access the VM hosting PostgreSQL

You are developing an application that will allow clients to download a file from your website for a specific period of time. How should you design the application to complete this task while following Google-recommended best practices?

Generate and assign a Cloud Storage-signed URL for the file. Make the URL available for the client to download.

Configure the application to send the file to the client as an email attachment.

Generate and assign a Cloud Storage-signed URL for the file. Make the URL available for the client to download.

Create a temporary Cloud Storage bucket with time expiration specified, and give download permissions to the bucket. Copy the file, and send it to the client.

Generate the HTTP cookies with time expiration specified. If the time is valid, copy the file from the Cloud Storage bucket, and make the file available for the client to download.

Your development team has been asked to refactor an existing monolithic application into a set of composable microservices. Which design aspects should you implement for the new application? (Choose two.)

Create an API contract agreement between the microservice implementation and microservice caller.

Implement a versioning scheme to permit future changes that could be incompatible with the current interface.

Develop the microservice code in the same programming language used by the microservice caller.

Create an API contract agreement between the microservice implementation and microservice caller.

Require asynchronous communications between all microservice implementations and microservice callers.

Ensure that sufficient instances of the microservice are running to accommodate the performance requirements.

Implement a versioning scheme to permit future changes that could be incompatible with the current interface.

You deployed a new application to Google Kubernetes Engine and are experiencing some performance degradation. Your logs are being written to Cloud Logging, and you are using a Prometheus sidecar model for capturing metrics. You need to correlate the metrics and data from the logs to troubleshoot the performance issue and send real-time alerts while minimizing costs. What should you do?

Export the Prometheus metrics and use Cloud Monitoring to view them as external metrics. Configure Cloud Monitoring to create log-based metrics from the logs, and correlate them with the Prometheus data.

Create custom metrics from the Cloud Logging logs, and use Prometheus to import the results using the Cloud Monitoring REST API.

Export the Cloud Logging logs and the Prometheus metrics to Cloud Bigtable. Run a query to join the results, and analyze in Google Data Studio.

Export the Cloud Logging logs and stream the Prometheus metrics to BigQuery. Run a recurring query to join the results, and send notifications using Cloud Tasks.

Export the Prometheus metrics and use Cloud Monitoring to view them as external metrics. Configure Cloud Monitoring to create log-based metrics from the logs, and correlate them with the Prometheus data.

You have been tasked with planning the migration of your company's application from on-premises to Google Cloud. Your company's monolithic application is an ecommerce website. The application will be migrated to microservices deployed on Google Cloud in stages. The majority of your company's revenue is generated through online sales, so it is important to minimize risk during the migration. You need to prioritize features and select the first functionality to migrate. What should you do?

Migrate the Product catalog, which has integrations to the frontend and product database.

Migrate Payment processing, which has integrations to the frontend, order database, and third-party payment vendor.

Migrate Order fulfillment, which has integrations to the order database, inventory system, and third-party shipping vendor.

Migrate the Shopping cart, which has integrations to the frontend, cart database, inventory system, and payment processing system.

Your team develops services that run on Google Kubernetes Engine. Your team's code is stored in Cloud Source Repositories. You need to quickly identify bugs in the code before it is deployed to production. You want to invest in automation to improve developer feedback and make the process as efficient as possible. What should you do?

Use Spinnaker to automate building container images from code based on Git tags.

Use Cloud Build to automate building container images from code based on Git tags.

Use Spinnaker to automate deploying container images to the production environment.

Use Cloud Build to automate building container images from code based on forked versions.

You manage an ecommerce application that processes purchases from customers who can subsequently cancel or change those purchases. You discover that order volumes are highly variable and the backend order-processing system can only process one request at a time. You want to ensure seamless performance for customers regardless of usage volume. It is crucial that customers' order update requests are performed in the sequence in which they were generated. What should you do?

Use a Pub/Sub subscriber in pull mode and use a data store to manage ordering.

Send the purchase and change requests over WebSockets to the backend.

Send the purchase and change requests as REST requests to the backend.

Use a Pub/Sub subscriber in pull mode and use a data store to manage ordering.

Use a Pub/Sub subscriber in push mode and use a data store to manage ordering.

Your company needs a database solution that stores customer purchase history and meets the following requirements:Customers can query their purchase immediately after submission.Purchases can be sorted on a variety of fields.Distinct record formats can be stored at the same time.Which storage option satisfies these requirements?

Cloud Storage using an object read

Cloud SQL using a SQL SELECT statement

Firestore in Datastore mode using a global query

You recently developed a new service on Cloud Run. The new service authenticates using a custom service and then writes transactional information to a Cloud Spanner database. You need to verify that your application can support up to 5,000 read and 1,000 write transactions persecond while identifying any bottlenecks that occur. Your test infrastructure must be able to autoscale. What should you do?

Create a Google Kubernetes Engine cluster running the Locust or JMeter images to dynamically generate load tests. Analyze the results using Cloud Trace.

Build a test harness to generate requests and deploy it to Cloud Run. Analyze the VPC Flow Logs using Cloud Logging.

Create a Google Kubernetes Engine cluster running the Locust or JMeter images to dynamically generate load tests. Analyze the results using Cloud Trace.

Create a Cloud Task to generate a test load. Use Cloud Scheduler to run 60,000 Cloud Task transactions perminute for 10minutes. Analyze the results using Cloud Monitoring.

Create a Compute Engine instance that uses a LAMP stack image from the Marketplace, and use Apache Bench to generate load tests against the service. Analyze the results using Cloud Trace.

You are using Cloud Build for your CI/CD pipeline to complete several tasks, including copying certain files to Compute Engine virtual machines. Your pipeline requires a flat file that is generated in one builder in the pipeline to be accessible by subsequent builders in the same pipeline. How should you store the file so that all the builders in the pipeline can access it?

Output the file contents to a file in /workspace. Read from the same /workspace file in the subsequent build step.

Store and retrieve the file contents using Compute Engine instance metadata.

Output the file contents to a file in /workspace. Read from the same /workspace file in the subsequent build step.

Use gsutil to output the file contents to a Cloud Storage object. Read from the same object in the subsequent build step.

Add a build argument that runs an HTTP POST via curl to a separate web server to persist the value in one builder. Use an HTTP GET via curl from the subsequent build step to read the value.

Your company's development teams want to use various open source operating systems in their Docker builds. When images are created in published containers in your company's environment, you need to scan them for Common Vulnerabilities and Exposures (CVEs). The scanning process must not impact software development agility. You want to use managed services where possible. What should you do?

Enable the Vulnerability scanning setting in the Container Registry.

Create a Cloud Function that is triggered on a code check-in and scan the code for CVEs.

Disallow the use of non-commercially supported base images in your development environment.

Use Cloud Monitoring to review the output of Cloud Build to determine whether a vulnerable version has been used.

You are configuring a continuous integration pipeline using Cloud Build to automate the deployment of new container images to Google Kubernetes Engine (GKE). The pipeline builds the application from its source code, runs unit and integration tests in separate steps, and pushes the container to Container Registry. The application runs on a Python web server.The Dockerfile is as follows:FROM python:3.7-alpine -COPY . /app -WORKDIR /app -RUN pip install -r requirements.txtCMD [ 'gunicorn', '-w 4', 'main:app' ]You notice that Cloud Build runs are taking longer than expected to complete. You want to decrease the build time. What should you do? (Choose two.)

Select a virtual machine (VM) size with higher CPU for Cloud Build runs.

Cache the Docker image for subsequent builds using the -- cache-from argument in your build config file.

Select a virtual machine (VM) size with higher CPU for Cloud Build runs.

Deploy a Container Registry on a Compute Engine VM in a VPC, and use it to store the final images.

Cache the Docker image for subsequent builds using the -- cache-from argument in your build config file.

Change the base image in the Dockerfile to ubuntu:latest, and install Python 3.7 using a package manager utility.

Store application source code on Cloud Storage, and configure the pipeline to use gsutil to download the source code.

You are building a CI/CD pipeline that consists of a version control system, Cloud Build, and Container Registry. Each time a new tag is pushed to the repository, a Cloud Build job is triggered, which runs unit tests on the new code builds a new Docker container image, and pushes it into Container Registry. The last step of your pipeline should deploy the new container to your production Google Kubernetes Engine (GKE) cluster. You need to select a tool and deployment strategy that meets the following requirements:* Zero downtime is incurred* Testing is fully automated* Allows for testing before being rolled out to users* Can quickly rollback if neededWhat should you do?

Trigger another Cloud Build job that uses the Kubernetes CLI tools to deploy your new container to your GKE cluster, where you can perform a shadow test.

Trigger a Spinnaker pipeline configured as an A/B test of your new code and, if it is successful, deploy the container to production.

Trigger a Spinnaker pipeline configured as a canary test of your new code and, if it is successful, deploy the container to production.

Trigger another Cloud Build job that uses the Kubernetes CLI tools to deploy your new container to your GKE cluster, where you can perform a canary test.

Trigger another Cloud Build job that uses the Kubernetes CLI tools to deploy your new container to your GKE cluster, where you can perform a shadow test.

Google Professional Cloud Developer Practice Test 4 - Free Online Exam Questions 121-160

You want to create ''fully baked'' or ''golden'' Compute Engine images for your application. You need to bootstrap your application to connect to the appropriate database according to the environment the application is running on (test, staging, production). What should you do?

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