Professional-Cloud-Developer Google Certified Professional - Cloud Developer Questions and Answers

Remove the step that pushes the container to Artifact Registry.

Add the —cache-from argument to the Docker build step in your build config file.

Store image artifacts in a Cloud Storage bucket in the same region as the Cloud Run instance.

Deploy a new Docker registry in a VPC and use Cloud Build worker pools inside the VPC to run the build pipeline.

Use Container Registry to create a registry in each development team's project. Configure the Cloud Build

build to push the Docker image to the project's registry. Grant the operations team access to each

development team's registry.

Create a separate project for the operations team that has Container Registry configured. Assign

appropriate permissions to the Cloud Build service account in each developer team's project to allow

access to the operation team's registry.

Create a separate project for the operations team that has Container Registry configured. Create a Service

Account for each development team and assign the appropriate permissions to allow it access to the

operations team's registry. Store the service account key file in the source code repository and use it to

authenticate against the operations team's registry.

Create a separate project for the operations team that has the open source Docker Registry deployed on a

Compute Engine virtual machine instance. Create a username and password for each development team.

Store the username and password in the source code repository and use it to authenticate against the

operations team's Docker registry.

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.

Create a Google service account with BigQuery access. Add the JSON key to Secret Manager, and use the Go client library to access the JSON key.

Create a Google service account with BigQuery access. Add the Google service account JSON key as a Kubernetes secret, and configure the application to use this secret.

Create a Google service account with BigQuery access. Add the Google service account JSON key to Secret Manager, and use an init container to access the secret for the application to use.

Create a Google service account and a Kubernetes service account. Configure Workload Identity on the GKE cluster, and reference the Kubernetes service account on the application Deployment.

Include unit tests in their code, and prevent deployments to QA until all tests have a passing status.

Include performance tests in their code, and prevent deployments to QA until all tests have a passing status.

Create health checks for the QA environment, and redeploy the APIs at a later time if the environment is unhealthy.

Redeploy the APIs to App Engine using Traffic Splitting. Do not move QA traffic to the new versions if errors are found.

Use Google Kubernetes Engine (GKE) to run the application as a microservice. Run the MySQL database on a dedicated GKE node.

Use multiple Compute Engine instances to run MySQL to store state information. Use a Google Cloud-managed load balancer to distribute the load between instances. Use managed instance groups for scaling.

Use Memorystore to store session information and CloudSQL to store state information. Use a Google Cloud-managed load balancer to distribute the load between instances. Use managed instance groups for scaling.

Use a Cloud Storage bucket to serve the application as a static website, and use another Cloud Storage bucket to store user state information.

Use local SSDs to store state.

Put a memcache layer in front of MySQL.

Move the state storage to Cloud Spanner.

Replace the MySQL instance with Cloud SQL.

Create a service account and download its key. Use the key to authenticate to Cloud Key Management Service (KMS) to obtain the database credentials.

Create a service account and download its key. Use the key to authenticate to Cloud Key Management Service (KMS) to obtain a key used to decrypt the database credentials.

Create a service account and grant it the roles/iam.serviceAccountUser role. Impersonate as this account and authenticate using the Cloud SQL Proxy.

Grant the roles/secretmanager.secretAccessor role to the Compute Engine service account. Store and access the database credentials with the Secret Manager API.

Create manual subnets.

Create an auto mode subnet.

Create multiple peered VPCs.

Provision a single instance for NAT.

Cloud Spanner

Cloud Datastore

Cloud Memorystore as a cache

Separate Cloud SQL clusters for each region

Block all traffic on port 443.

Allow all traffic into the network.

Allow traffic on port 443 for a specific tag.

Allow all traffic on port 443 into the network.

BigQuery

Cloud SQL

Cloud Spanner

Cloud Datastore

Use Google App Engine services.

Use serverless Google Cloud Functions.

Use Knative to build and deploy serverless applications.

Use Google Kubernetes Engine for automated deployments.

Use a large Google Compute Engine cluster for deployments.

Split traffic between versions using weights.

Enable the new recommendation feature flag on a single instance.

Mirror traffic to the new version of your application.

Use HTTP header-based routing.

Perform a rolling deployment, and test your new application after the deployment is complete.

Perform A/B testing, and test your application periodically after the new tests are implemented.

Perform a blue/green deployment, and test your new application after the deployment is. complete.

Perform a canary deployment, and test your new application periodically after the new version is deployed.

Create a service to publish messages, and deploy the Pub/Sub emulator. Generate random content in the publishing service, and publish to the emulator.

Create a service to publish messages to your application. Collect the messages from Pub/Sub in production, and replay them through the publishing service.

Create a service to publish messages, and deploy the Pub/Sub emulator. Collect the messages from Pub/Sub in production, and publish them to the emulator.

Create a service to publish messages, and deploy the Pub/Sub emulator. Publish a standard set of testing messages from the publishing service to the emulator.

Ask the developers to use Cloud Shell and run gcloud container clusters get-credentials to switch to another cluster.

Ask the developers to open three terminals on their workstation and use kubecrt1 config to configure access to each cluster.

Ask the developers to install the gcloud CLI on their workstation and run gcloud container clusters get-credentials to switch to another cluster

In a configuration file, define the clusters users, and contexts Email the file to the developers and ask them to use kubect1 config to add cluster, user and context details.

Use the Cloud Data Loss Prevention API for redaction of the review dataset.

Use the Cloud Data Loss Prevention API for de-identification of the review dataset.

Use the Cloud Natural Language Processing API for redaction of the review dataset.

Use the Cloud Natural Language Processing API for de-identification of the review dataset.

Take frequent snapshots of all of the VMs.

Install the Stackdriver Logging agent on the VMs.

Install the Stackdriver Monitoring agent on the VMs.

Use Stackdriver Trace to look for performance bottlenecks.

Use the current single instance MySQL on Compute Engine and several read-only MySQL servers on

Compute Engine.

Use the current single instance MySQL on Compute Engine, and replicate the data to Cloud SQL in an

external master configuration.

Replace the current single instance MySQL instance with Cloud SQL, and configure high availability.

Replace the current single instance MySQL instance with Cloud SQL, and Google provides redundancy

without further configuration.

Create new Cloud SQL instances in Europe and North America for testing and deployment. Provide developers with local MySQL instances to conduct testing on the application changes.

Migrate data to Bigtable. Instruct the development teams to use the Cloud SDK to emulate a local Bigtable development environment.

Move from Cloud SQL to MySQL hosted on Compute Engine. Replicate hosts across regions in the Americas and Europe. Provide developers with local MySQL instances to conduct testing on the application changes.

Migrate data to Firestore in Native mode and set up instan

Create an API key. Use the API key to interact with Google Cloud.

Use the default compute service account to interact with Google Cloud.

Create a service account for the application. Export and deploy the private key for the application. Use the service account to interact with Google Cloud.

Create a service account for the application and for each Google Cloud API used by the application. Export and deploy the private keys used by the application. Use the service account with one Google Cloud API to interact with Google Cloud.

Cloud Profiler

Cloud Monitoring

Cloud Trace

Cloud Logging

Cloud Armor

Cloud Functions

Cloud Endpoints

Shielded Virtual Machines

Use App Engine for autoscaling.

Use Cloud Functions for autoscaling.

Use a Compute Engine cluster for the service.

Use a dedicated Compute Engine virtual machine instance for the service.

Cloud VPN

Cloud Armor

Virtual Private Cloud

Cloud Identity-Aware Proxy

Migrate the database to Bigtable and use it to serve all global user traffic.

Migrate the database to Cloud Spanner and use it to serve all global user traffic.

Migrate the database to Firestore in Datastore mode and use it to serve all global user traffic.

Migrate the services to Google Kubernetes Engine and use a load balancer service to better scale the application.

Bind the user identity to the pubsub.publisher and pubsub.subscriber roles at the resource level.

Grant the user identity the pubsub.publisher and pubsub.subscriber roles at the project level.

Grant the user identity a custom role that contains the pubsub.topics.create and pubsub.subscriptions.create permissions.

Configure the application to run as a service account that has the pubsub.publisher and pubsub.subscriber roles.

Add a Stackdriver counter metric for path:/api/alpha/.

Add a Stackdriver counter metric for endpoint:/api/alpha/*.

Export the logs to Cloud Storage and count lines matching /api/alphA.

Export the logs to Cloud Pub/Sub and count lines matching /api/alphA.

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.

Use canary deployment

Use blue/green deployment

Use rolling updates deployment

Use A/B testing with traffic mirroring during deployment

Configure Cloud Build to deploy the Cloud Function. If the code passes the tests, a deployment approval is sent to you.

Configure Cloud Build to deploy the Cloud Function, using the specific service account as the build agent. Run the unit tests after successful deployment.

Configure Cloud Build to run the unit tests. If the code passes the tests, the developer deploys the Cloud Function.

Configure Cloud Build to run the unit tests, using the specific service account as the build agent. If the code passes the tests, Cloud Build deploys the Cloud Function.

Embed the appropriate database connection string in the image. Create a different image for each environment.

When creating the Compute Engine instance, add a tag with the name of the database to be connected. In your application, query the Compute Engine API to pull the tags for the current instance, and use the tag to construct the appropriate database connection string.

When creating the Compute Engine instance, create a metadata item with a key of “DATABASE” and a value for the appropriate database connection string. In your application, read the “DATABASE” environment variable, and use the value to connect to the appropriate database.

When creating the Compute Engine instance, create a metadata item with a key of “DATABASE” and a value for the appropriate database connection string. In your application, query the metadata server for the “DATABASE” value, and use the value to connect to the appropriate database.

Uptime check

Process health

Metric absence

Metric threshold

Include multiple rows with each request.

Perform the inserts in parallel by creating multiple threads.

Write each row to a Cloud Storage object, then load into BigQuery.

Write each row to a Cloud Storage object in parallel, then load into BigQuery.