PCD Deploying applications — Timed 20 Questions

Question 1

A team deploys a containerized web application on Google Kubernetes Engine (GKE) using a Deployment. They need to expose the application externally via a stable IP address and enable SSL termination. Which resource should they use?

Accepted Answer

Ingress with Google-managed SSL certificate. An Ingress with a Google-managed SSL certificate is the correct choice because it provides a single stable IP address via a global forwarding rule, terminates SSL at the Google Cloud HTTP(S) load balancer, and routes traffic to the GKE Deployment. This approach offloads SSL decryption from the application pods and uses a managed certificate that auto-renews, meeting both the stable IP and SSL termination requirements.

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HorizontalPodAutoscaler

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Service type NodePort

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Service type LoadBalancer

Question 2

During a deployment to App Engine flexible environment, the new version fails to start and the logs show 'Container failed to start: context deadline exceeded'. The previous version remains serving traffic. What is the most likely cause?

Accepted Answer

The container startup time exceeds the 10-minute timeout.. Option C is correct because the error 'context deadline exceeded' in App Engine flexible environment indicates that the container did not start within the allowed startup timeout. The default timeout for container startup in App Engine flexible is 10 minutes, and if the application takes longer (e.g., due to slow initialization, large dependency downloads, or database migrations), the platform kills the container and logs this error. The previous version continues serving because the new version failed to become healthy.

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The health check is misconfigured, causing the instance to be considered unhealthy.

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The app requires an environment variable that is not set.

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The Dockerfile has a syntax error that prevents the container from building.

Question 3

A company has a multi-region Cloud Run service with traffic splitting between revisions. They notice that a newly rolled-out revision is receiving 0% of traffic even though they set traffic to 100% via the console. The revision shows 'Ready: Yes'. What is the most likely cause?

Accepted Answer

The revision has a tag but no traffic percentage assigned; the tag is being used for routing.. When a revision shows 'Ready: Yes' but receives 0% traffic despite setting 100% via the console, the most likely cause is that the revision has a tag assigned but no traffic percentage. In Cloud Run, tags are used for direct URL routing (e.g., for testing) and do not receive any traffic from the service's main URL unless a traffic percentage is explicitly assigned. The console's traffic splitting UI allows setting a tag without a percentage, which can lead to this confusion.

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The revision has a low CPU limit causing it to be throttled.

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The revision is not healthy because of a misconfigured health check.

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The revision has a concurrency setting of 0, which is invalid.

Question 4

A DevOps engineer is automating deployments to Compute Engine using a CI/CD pipeline. They want to minimize downtime and ensure that if a new VM fails health checks, the old VM continues serving. Which deployment strategy should they implement?

Accepted Answer

Blue/green deployment with health checks and a managed instance group. Blue/green deployment with health checks and a managed instance group is correct because it allows the new version (green) to be fully deployed and validated against health checks before any traffic is switched from the old version (blue). If the new VM fails health checks, the managed instance group automatically keeps the old version serving, ensuring zero downtime and immediate rollback without manual intervention.

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Redeploy the old version manually if the new version fails

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Rolling update with a readiness probe

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Canary deployment with a small percentage of traffic

Question 5

A developer deploys a Cloud Function (2nd gen) that processes messages from Pub/Sub. The function sometimes fails with 'Deadline Exceeded' for messages that take longer than 9 minutes. What should the developer do to handle these long-running messages without losing them?

Accepted Answer

Set the Pub/Sub subscription acknowledgment deadline to 10 minutes and implement a push endpoint that acknowledges after processing.. Option C is correct because Cloud Functions (2nd gen) have a maximum timeout of 60 minutes, but Pub/Sub push subscriptions have a default acknowledgment deadline of 10 seconds. By setting the acknowledgment deadline to 10 minutes and implementing a push endpoint that acknowledges after processing, the developer ensures the message is not redelivered prematurely while allowing the function up to 10 minutes to complete. This prevents 'Deadline Exceeded' errors for messages that take longer than 9 minutes without losing messages, as the subscription will wait for the acknowledgment before considering the message as failed.

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Configure the function to retry on failure and set a maximum retry count.

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Increase the Cloud Function timeout to 60 minutes.

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Use Cloud Tasks instead of Pub/Sub for asynchronous invocation.

Question 6

Which TWO statements about deploying applications on Google Kubernetes Engine (GKE) are correct?

Accepted Answer

HorizontalPodAutoscaler can use custom metrics from Cloud Monitoring.. HorizontalPodAutoscaler (HPA) in GKE can scale pods based on custom metrics from Cloud Monitoring (formerly Stackdriver). This is achieved by using the custom.metrics.k8s.io API, which allows HPA to query metrics like custom application latency or queue depth, not just default CPU/memory. This enables fine-grained, application-specific autoscaling.

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Kubernetes Secrets are encrypted at rest by default.

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A zonal GKE cluster automatically uses regional persistent disks for high availability.

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To expose a Deployment externally, you must create an Ingress resource.

Question 7

Which THREE practices should be followed when deploying a containerized application to Cloud Run?

Accepted Answer

Avoid writing to the local filesystem for data that must persist across requests.. Option A is correct because Cloud Run instances are ephemeral and the local filesystem is not persisted across requests or instance restarts. Writing to local disk for data that must survive beyond a single request will cause data loss when the instance is recycled, which is a fundamental characteristic of serverless container platforms.

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Set a maximum request timeout of 10 minutes to avoid cold starts.

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Hardcode port 8080 in the container.

Question 8

A developer runs the above command and receives the error. What is the most likely cause?

Accepted Answer

The cloudbuild.yaml file has a syntax error, such as incorrect indentation.. The error is most likely due to a syntax error in the cloudbuild.yaml file, such as incorrect indentation. Cloud Build uses YAML for configuration, and YAML is sensitive to indentation; a missing space or incorrect alignment can cause the build to fail with a parsing error. The command `gcloud builds submit` reads the cloudbuild.yaml file from the current directory, and if the YAML is malformed, the submission will fail before any Docker or build steps are executed.

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The image tag format is incorrect.

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The cloudbuild.yaml file is not present in the current directory.

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The Dockerfile is missing from the repository.

Question 9

A developer deploys the above app.yaml to App Engine standard environment. The deployment succeeds, but the application fails to connect to the database. What is the most likely reason?

Accepted Answer

The application is trying to connect to a local database on localhost, which is not available in the App Engine sandbox.. Option C is correct because in App Engine standard environment, applications run in a sandboxed environment that does not support connections to a local database on localhost. The application code is attempting to connect to a database at 127.0.0.1 or localhost, which is not available in the sandbox. Instead, the application must connect to a Cloud SQL instance using a Unix socket or a private IP, or use a fully managed database service like Firestore.

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The runtime 'python39' is not supported in App Engine standard environment.

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The $PORT environment variable is not set in App Engine standard environment.

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The entrypoint command is incorrect because gunicorn is not allowed.

Question 10

A company wants to deploy a containerized application on Google Kubernetes Engine (GKE) with zero downtime during updates. The application is stateless and runs on a Deployment with 5 replicas. Which deployment strategy should be used?

Accepted Answer

Rolling update. A rolling update is the default deployment strategy in Kubernetes and is ideal for stateless applications requiring zero downtime. It gradually replaces old Pods with new ones, ensuring that a minimum number of replicas remain available throughout the update. This strategy is configured via the `strategy.type: RollingUpdate` field in the Deployment spec, with parameters like `maxSurge` and `maxUnavailable` controlling the pace.

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Blue/green deployment

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Recreate update

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Canary deployment

Question 11

A developer is deploying a Cloud Run service that needs to access a Cloud SQL instance. The service is deployed with the --no-allow-unauthenticated flag. What is the recommended way to grant the service access to the database?

Accepted Answer

Grant the Cloud SQL Client role to the Cloud Run service's runtime service account.. The recommended way to grant a Cloud Run service access to a Cloud SQL instance is to grant the Cloud SQL Client role (roles/cloudsql.client) to the Cloud Run service's runtime service account. This role provides the necessary permissions (cloudsql.instances.connect and cloudsql.instances.get) to establish a secure connection via the Cloud SQL Auth Proxy or the Cloud SQL connector library. Using the runtime service account follows the principle of least privilege and avoids managing long-lived credentials.

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Create a service account key and store it in Secret Manager, then mount it as a volume.

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Use the default Compute Engine service account and grant it the Cloud SQL Client role.

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Enable the Cloud SQL Admin API and use Application Default Credentials from the Cloud Run service.

Question 12

A company is deploying a microservices architecture on GKE. They need to expose a set of related microservices under a single external IP address with path-based routing. Which Kubernetes resource should they use?

Accepted Answer

Ingress resource. An Ingress resource is the correct choice because it provides HTTP/HTTPS layer-7 routing to expose multiple services under a single external IP address, using path-based or host-based rules. This directly meets the requirement of exposing a set of related microservices with path-based routing on GKE, whereas a Service of type LoadBalancer would create a separate external IP per service.

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Service of type NodePort

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NetworkPolicy

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Service of type LoadBalancer

Question 13

A developer needs to deploy a Python application to App Engine flexible environment. The application requires a specific version of a system package (libssl-dev) that is not included in the default runtime image. How should the developer install this package?

Accepted Answer

Create a Dockerfile that uses the base runtime image and runs apt-get install.. Option C is correct because the App Engine flexible environment runs your application in a Docker container based on a Google-provided runtime image. To install system packages like libssl-dev that are not included in the default image, you must customize the container by creating a Dockerfile that starts FROM the base runtime image and then runs apt-get install. This is the standard method for adding OS-level dependencies in the flexible environment.

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Use a custom runtime that already includes the package.

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Specify the package in the app.yaml file under the 'libraries' section.

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Add the package name to the requirements.txt file.

Question 14

Which TWO of the following are best practices when deploying applications on Google Kubernetes Engine (GKE)?

Accepted Answer

Use pod anti-affinity to spread pods across nodes.. Option C is correct because pod anti-affinity ensures pods are scheduled across different nodes, improving fault tolerance and high availability. This is a best practice for stateless applications to avoid a single point of failure during node failures. Option D is correct because defining resource requests and limits allows the Kubernetes scheduler to make informed placement decisions and prevents resource starvation, ensuring predictable application performance.

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Store sensitive configuration data in environment variables.

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Skip liveness and readiness probes for stateless applications.

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Use the default Compute Engine service account for pods.

Question 15

A developer runs the above command to build and push a container image to Container Registry, but receives the error shown. The developer has the 'Cloud Build Editor' role on the project. What is the most likely cause of the error?

Accepted Answer

The Cloud Build service account has not been enabled or does not have permission to act on behalf of the user.. The error occurs because the Cloud Build service account (typically the Compute Engine default service account or a user-specified service account) lacks the necessary permissions to push the container image to Container Registry. Even though the developer has the 'Cloud Build Editor' role, Cloud Build itself needs a service account with appropriate IAM roles (e.g., Storage Object Admin) to write to the registry. The error is not about the developer's direct permissions but about the service account that Cloud Build uses to execute the build and push.

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The Cloud Storage bucket for storing build artifacts does not exist.

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The developer's user account has been revoked access to the project.

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The developer does not have the 'cloudbuild.builds.create' permission because the Cloud Build Editor role does not include it.

Question 16

You are deploying a critical application on Compute Engine. The application requires high availability and must survive a zonal failure. You have created a managed instance group (MIG) with autoscaling across two zones. The application state is stored in a Cloud SQL instance with a read replica in another region. The application also uses a shared static IP address for client access. During a test, you simulate a failure of zone us-central1-a. You observe that the MIG automatically creates new instances in the remaining zone, but the application becomes unreachable for several minutes. What is the most likely cause of the downtime?

Accepted Answer

The load balancer's health check interval and timeout caused a delay in marking the backend instances as unhealthy and routing traffic to the new zone.. The most likely cause is that the load balancer's health check interval and timeout delayed the detection of unhealthy instances in the failed zone, preventing traffic from being rerouted to the new instances in the remaining zone. Even though the MIG created new instances quickly, the load balancer continued sending requests to the failed zone until the health check marked those backends as unhealthy, causing the application to be unreachable during that window.

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The static IP address was not configured to failover to the remaining zone.

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The Cloud SQL read replica did not promote to primary quickly enough.

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The managed instance group's autoscaler took too long to create new instances in the remaining zone.

Question 17

Your company is deploying a web application on Cloud Run using a continuous deployment pipeline from Cloud Build. The application is built as a Docker container and pushed to Container Registry. The Cloud Run service is configured with the '--no-allow-unauthenticated' flag. You have set up Cloud Build triggers to build and deploy on commits to the main branch. The deployment works correctly for the first few commits, but after adding a new environment variable in the Cloud Build configuration file (cloudbuild.yaml), the deployment fails with an error that the Cloud Run service cannot be updated because the new revision fails health checks. The application code has not changed. What is the most likely cause?

Accepted Answer

The new environment variable causes the application to fail its startup or health check.. Option D is correct because the application code has not changed, yet the deployment fails health checks immediately after adding a new environment variable. This indicates that the application is likely reading that variable at startup and crashing or failing its readiness probe due to an invalid value, missing dependency, or misconfiguration. Cloud Run requires the new revision to pass health checks (e.g., HTTP GET on the configured port) before it can serve traffic; if the variable causes the app to exit or hang, the revision is considered unhealthy and the update is rejected.

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The Cloud Build service account does not have permission to update the Cloud Run service.

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The new environment variable exceeds the maximum size limit for environment variables in Cloud Run.

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The health check configuration in the Cloud Run service was overwritten by the new deployment.

Question 18

A company is deploying a containerized application on Google Kubernetes Engine (GKE). The development team has built a Docker image and pushed it to Artifact Registry. They want to automate the deployment process so that whenever a new image is pushed to the registry, the application is automatically updated in the GKE cluster. Which combination of services should they use to achieve this?

Accepted Answer

Set up a Cloud Build trigger that monitors the Artifact Registry and runs a build step to update the GKE deployment using kubectl.. Option B is correct because Cloud Build can be configured with a trigger that monitors Artifact Registry for new image pushes. When a new image is pushed, the trigger executes a build step that uses kubectl to update the GKE deployment, enabling continuous deployment without manual intervention.

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Use Cloud Deploy to create a delivery pipeline that watches the Artifact Registry and promotes the image to GKE.

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Schedule a Cloud Scheduler job that periodically checks for new images in Artifact Registry and updates the GKE deployment.

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Configure a Cloud Run service that is automatically deployed when a new image is pushed to Artifact Registry.

Question 19

A team is deploying a microservice application on Google Kubernetes Engine (GKE). They want to ensure high availability and minimize downtime during rolling updates. Which TWO actions should they take? (Choose two.)

Accepted Answer

Configure pod disruption budgets to limit the number of pods that can be unavailable simultaneously.. Option C is correct because PodDisruptionBudgets (PDBs) allow you to specify the minimum number of pods that must remain available during voluntary disruptions like rolling updates, ensuring high availability. Option D is correct because readiness probes control when a pod is added to a Service's endpoints; during rolling updates, they prevent traffic from being sent to a pod until it is ready, minimizing downtime.

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Use Horizontal Pod Autoscaler to automatically adjust the number of pods based on CPU utilization.

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Enable liveness probes to automatically restart pods that become unresponsive.

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Enable node auto-repair to automatically replace unhealthy nodes.

Question 20

Refer to the exhibit. A developer is deploying a container to Cloud Run and receives the error shown. What is the most likely cause?

Accepted Answer

The container is trying to listen on a privileged port (e.g., 80) instead of the expected port 8080.. Cloud Run requires containers to listen on the port specified by the PORT environment variable, which defaults to 8080. The error indicates the container is trying to bind to port 80, a privileged port, which is not allowed by the Cloud Run runtime sandbox. This mismatch causes the container to fail health checks and deployment.

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The container's health check is failing because the startup command is incorrect.

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The PORT environment variable is not set correctly in the Cloud Run service configuration.

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The container image does not exist in the specified registry.