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?

• A.Configure the appropriate service accounts, and use Workload Identity to run the pods.
• B.Store the application credentials as Kubernetes Secrets, and expose them as environment variables.
• C.Configure the appropriate routing rules, and use a VPC-native cluster to directly connect to the database.
• D.Store the application credentials using Cloud Key Management Service, and retrieve them whenever a database connection is made.

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You are using Cloud Run to host a web application. You need to securely obtain the application project ID and region where the application is running and display this information to users. You want to use the most performant approach. What should you do?

• A.Make an API call to the Cloud Asset Inventory API from the application and format the request to include instance metadata.
• B.Use HTTP requests to query the available metadata server at the http://metadata.google.internal/ endpoint with the Metadata-Flavor: Google header.
• C.In the Google Cloud console, navigate to the Project Dashboard and gather configuration details.
  Navigate to the Cloud Run "Variables & Secrets" tab, and add the desired environment variables in Key:Value format.
• D.In the Google Cloud console, navigate to the Project Dashboard and gather configuration details. Write the application configuration information to Cloud Run's in-memory container filesystem.

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Your website is deployed on Compute Engine. Your marketing team wants to test conversion rates between 3 different website designs. You are not able to make changes to your application code. What should you do?

• A.Deploy website on App Engine and use traffic splitting.
• B.Deploy website on App Engine as three separate services.
• C.Deploy website on Cloud Functions and implement custom code to show different designs.
• D.Deploy website on Cloud Functions as three separate functions.

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You are developing an online chat application where users can upload profile pictures. Uploaded profile pictures must comply with content policies. You need to detect inappropriate images and label those images automatically when they are uploaded. In the future, this process will need to be expanded to include additional processing tasks such as watermarking and image compression.
You want to simplify orchestration and minimize operational overhead of the image scanning and labeling steps while also ensuring that additional steps can be added and removed easily later on. What should you do?

• A.Save user-uploaded images to a temporary Cloud Storage bucket. Implement code on the backend server to retrieve the image content and call the Vision API to process each new uploaded image.
• B.Save user-uploaded images to a Cloud Storage bucket. Configure a Cloud Run function that is triggered when a new image is uploaded and calls one or more Cloud Run services. Create additional Cloud Run services that call the Vision API to process each new uploaded image.
• C.Save user-uploaded images to a Cloud Storage bucket. Configure a Cloud Run function that is triggered when a new image is uploaded and publishes a message to a Pub/Sub topic. Deploy microservices in GKE that subscribe to the Pub/Sub topic and call the Vision API to process each new uploaded image.
• D.Save user-uploaded images to a Cloud Storage bucket. Create an Eventarc trigger that connects the bucket to the Workflows event receiver when a new image is uploaded. Create a workflow in Workflows with multiple Cloud Run functions that call the Vision API to process each new uploaded image.

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The new version of your containerized application has been tested and is ready to deploy to production on Google Kubernetes Engine. You were not able to fully load-test the new version in pre-production environments, and you need to make sure that it does not have performance problems once deployed. Your deployment must be automated. What should you do?

• A.Use Cloud Load Balancing to slowly ramp up traffic between versions. Use Cloud Monitoring to look for performance issues.
• B.Deploy the application via a continuous delivery pipeline using canary deployments. Use Cloud Monitoring to look for performance issues. and ramp up traffic as the metrics support it.
• C.Deploy the application via a continuous delivery pipeline using blue/green deployments. Use Cloud Monitoring to look for performance issues, and launch fully when the metrics support it.
• D.Deploy the application using kubectl and set the spec.updateStrategv.type to RollingUpdate. Use Cloud Monitoring to look for performance issues, and run the kubectl rollback command if there are any issues.

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