PCD Practice Test 12 — 15 Questions

Question 1

Match each Kubernetes resource to its function.

Accepted Answer

Pod: The smallest and simplest Kubernetes object. It represents a single instance of a running process in your cluster.. These are fundamental Kubernetes objects for running containerized applications.

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ConfigMap: Stores sensitive data such as passwords, OAuth tokens, and ssh keys.

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Secret: Manages external access to the services in a cluster, typically HTTP.

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Ingress: Allows you to decouple configuration artifacts from image content to keep containerized applications portable.

Question 2

Which two statements are true about Cloud Load Balancing? (Choose two.)

Accepted Answer

Global external HTTP(S) load balancer can distribute traffic across multiple regions.. The global external HTTP(S) load balancer is a proxy-based, Layer 7 load balancer that uses a single anycast IP address and can distribute traffic across backend instances in multiple regions, enabling global load balancing with automatic failover. Option D is correct because the internal TCP/UDP load balancer is a regional, pass-through load balancer that operates at Layer 4 and is designed to distribute traffic among instances within the same VPC and region, using RFC 1918 private IP addresses.

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All load balancers support IPv6 client traffic.

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SSL proxy load balancer supports non-HTTP traffic.

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Network load balancer can only balance TCP traffic.

Question 3

A company runs a critical financial application on Google Cloud using Compute Engine instances in a managed instance group (MIG) with auto-scaling based on CPU utilization. The application stores state in a local SSD and relies on sticky sessions (session affinity). Recently, during a traffic spike, the MIG scaled out new instances, but some users lost their sessions because the load balancer routed them to a different instance. The team needs to maintain session persistence without sacrificing scalability. What should they do?

Accepted Answer

Implement a shared session store using Cloud Memorystore for Redis.. Using Cloud Memorystore for Redis provides a centralized, external session store that decouples session state from individual Compute Engine instances. This ensures that any instance in the managed instance group can serve any user's request, maintaining session persistence even as the MIG scales out or in based on CPU utilization. It preserves scalability because the session store is independent of instance lifecycle, and Redis offers low-latency reads and writes suitable for session data.

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Increase the instance group's cooldown period to reduce scaling frequency.

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Use a global HTTPS Load Balancer with cookie-based session affinity.

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Use Cloud NAT for consistent source IP routing.

Question 4

A company uses Cloud Deploy for continuous delivery with multiple targets (dev, staging, prod). After a successful promotion to staging, the team discovers a critical bug and needs to roll back the production target to the previous release. The production target has already been promoted to the current release, but the staging target should remain on the current release. How should the team roll back the production target?

Accepted Answer

Use the 'gcloud deploy rollback' command targeting the production target.. The 'gcloud deploy rollback' command is specifically designed to roll back a Cloud Deploy target to its previous successful release without affecting other targets. This command reverts the production target to the prior release while leaving the staging target on the current release, as required. It operates by redeploying the last known good release to the specified target, ensuring minimal disruption and preserving the promotion history.

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Create a new release with the same image tag as the previous release and promote it to production.

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Redeploy the previous release by running the previous Cloud Deploy command.

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Manually delete the current release and then promote the previous release again.

Question 5

A company uses Cloud Monitoring to set up an alerting policy for CPU utilization on Compute Engine instances. They want to be notified when average CPU usage exceeds 80% for 5 minutes. Which threshold type should they use?

Accepted Answer

Threshold. A Threshold alerting policy in Cloud Monitoring triggers when a metric's value crosses a defined static boundary. For this use case, setting a threshold of 80% with a duration of 5 minutes directly matches the requirement to alert when average CPU usage exceeds 80% for that period.

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Forecast

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Change rate

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Metric absence

Question 6

You are a site reliability engineer for a fintech company that runs a latency-sensitive trading application on Google Kubernetes Engine (GKE). The application is instrumented with OpenTelemetry and exports traces and metrics to Cloud Monitoring and Cloud Logging. Recently, the team observed a gradual increase in p99 latency from 50ms to 500ms over the past week, and error rates have spiked to 5% from a baseline of 0.1%. You review the Cloud Monitoring dashboards and notice that the 'container/cpu/utilization' metric shows normal usage, but the 'container/memory/bytes_used' metric shows a steady climb, reaching 90% of the memory limit on several pods. The application logs contain many 'OutOfMemoryError' exceptions and 'GC overhead limit exceeded' messages. You also see that the HPA (Horizontal Pod Autoscaler) has not triggered any scale-up events because the 'custom/googleapis.com|container/cpu/utilization' metric is below the target utilization threshold. The cluster autoscaler is enabled and has sufficient node pool capacity. What is the most likely root cause and the best immediate action to resolve the issue?

Accepted Answer

Add a custom metric for memory utilization to the HPA and configure the target to scale when memory exceeds 70%.. The gradual memory increase and OutOfMemoryError exceptions indicate that the application is memory-bound, not CPU-bound. Since the HPA is configured to scale only on CPU utilization, it never triggers scale-up despite memory pressure. Adding a custom memory utilization metric to the HPA (option D) directly addresses the root cause by scaling pods when memory exceeds 70%, preventing OOM errors and reducing latency.

Answer

Enable the Vertical Pod Autoscaler (VPA) in update mode to automatically adjust memory requests.

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Switch the HPA to use the default 'container/cpu/utilization' metric instead of the custom metric.

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Increase the memory request and limit for the pods to allow more memory usage.

Question 7

An enterprise is migrating a 10 TB Teradata data warehouse to BigQuery with minimal downtime. They need to transform Teradata BTEQ scripts to equivalent BigQuery SQL. Which approach should the migration team take to handle the SQL dialect differences?

Accepted Answer

Use Cloud Data Fusion to transform BTEQ scripts. Cloud Data Fusion is a data integration service that provides a visual interface to design and execute data transformations. While it does not automatically convert BTEQ scripts, it can be used to manually rebuild the transformation logic into BigQuery SQL, making it the most suitable option for handling SQL dialect differences. BigQuery Data Transfer Service for Teradata only transfers data and schema, not SQL scripts.

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Use BigQuery Data Transfer Service for Teradata to automate the migration

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Use gcloud bigquery command-line tool

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Use Database Migration Service with Teradata connector

Question 8

Your application is deployed on Google Kubernetes Engine (GKE). You want to monitor resource usage at the pod level. Which tool should you use?

Accepted Answer

Cloud Monitoring with Kubernetes integration. Cloud Monitoring with Kubernetes integration is the correct choice because it provides native pod-level metrics such as CPU, memory, disk, and network usage by leveraging the Kubernetes API and cAdvisor. This integration automatically collects resource utilization from each pod without requiring manual instrumentation, making it ideal for monitoring resource usage at the pod level in GKE.

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Cloud Trace

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Cloud Logging

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Cloud Profiler

Question 9

A company is deploying a batch job that runs once a day on Compute Engine. They are using a startup script to install dependencies and run the job. The job writes output to Cloud Storage. Recently, the job started failing intermittently with "No space left on device" errors, even though the persistent disk has 100 GB free. The team has verified that the disk is not fragmented and that the inode usage is low. The job processes large files and creates many temporary files in /tmp. They suspect the /tmp directory is filling up. What is the most likely cause?

Accepted Answer

The /tmp partition is using a small temporary disk that is separate from the persistent disk.. On many Compute Engine images, /tmp is mounted as a tmpfs (in-memory filesystem) which has a limited size, often a fraction of the instance's memory. When the job creates many temporary files, it can fill the tmpfs, causing "No space left on device" even though the persistent disk has ample free space. Option B is incorrect because while cleanup would help, the root cause is limited space in /tmp. Option C is incorrect because insufficient RAM would cause swapping, not a filesystem full error. Option D is incorrect because Cloud Storage quota would produce errors when writing to the bucket, not on the local filesystem.

Answer

The instance's RAM is insufficient, causing swap to fill the disk.

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The startup script is not cleaning up temporary files.

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The Cloud Storage bucket quota is exceeded.

Question 10

A developer wants to receive notifications when the error rate of their application exceeds 1% over a 5-minute window. What should they create in Cloud Monitoring?

Accepted Answer

Alerting policy with metric threshold condition. An alerting policy with a metric threshold condition is the correct approach because Cloud Monitoring evaluates a metric (e.g., error rate) against a threshold (1%) over a specified window (5 minutes) and triggers a notification when the condition is met. This directly fulfills the requirement to be notified when the error rate exceeds the threshold, as alerting policies are designed for proactive notification based on metric data.

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Log-based metric

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Dashboard with error rate chart

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Uptime check

Question 11

A company uses Cloud Build to build and deploy a microservice. The build step that runs tests fails with a permission denied error when trying to access a private GitHub repository. The build configuration uses a default Cloud Build service account. The team has already added the GitHub repository as a trigger and provided credentials during trigger creation. However, the build step still fails. What is the most likely cause and solution?

Accepted Answer

Create a new service account with access to the secret containing the GitHub SSH key and use it in the build configuration.. Cloud Build uses the Cloud Build service account by default for executing build steps. To access private GitHub repositories, the build step must authenticate using the SSH key or access token stored in Secret Manager, and the Cloud Build service account needs permissions to read the secret. The error suggests the build step does not have the necessary authentication. Using a custom service account with required permissions and retrieving the secret in the build step is the correct approach. The trigger credentials are only for triggering the build, not for build steps.

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Use Cloud Build's '--no-cache' flag to force a fresh clone.

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Add a build step to run 'git config' to set user credentials.

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Grant the Cloud Build service account the role 'Cloud Build Service Agent'.

Question 12

An organization is migrating an on-premises PostgreSQL database to Cloud SQL. They want to minimize downtime during the cutover. Which Google Cloud service should they use to perform an online migration with near-zero downtime?

Accepted Answer

Database Migration Service. Database Migration Service (DMS) supports continuous migration jobs that perform a full dump, then CDC (change data capture), allowing for near-zero-downtime cutover by promoting the Cloud SQL replica.

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BigQuery Data Transfer Service

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Cloud Dataflow

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Transfer Appliance

Question 13

A developer deployed a Cloud Function that is triggered by a Pub/Sub topic. The function processes messages and writes results to a BigQuery table. The developer notices that some messages are not being processed; they are visible in the Pub/Sub subscription but the function logs show no invocation for those messages. The function's code is correct and handles errors gracefully. What is the most likely cause and fix?

Accepted Answer

Enable retry on failure for the Pub/Sub subscription.. The Pub/Sub subscription has a default 'ack deadline' and if the Cloud Function does not acknowledge the message within that time (or if the function fails to process it and the subscription is not configured to retry), the message may be redelivered but eventually dropped. Enabling retry on failure ensures that messages that cause the function to fail (even if the code handles errors gracefully, the function might still return an error status) are retried until successfully processed.

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Increase the function timeout to 540 seconds.

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Check that the Pub/Sub subscription push endpoint is set to the Cloud Function URL.

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Increase the function memory to 2GB.

Question 14

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.. 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 15

A multinational corporation runs a web application on Google Kubernetes Engine (GKE) with multiple microservices. They use Cloud Service Mesh (Anthos) for observability and security. The application uses gRPC for inter-service communication. Recently, they have observed increased latency and occasional timeouts between services in different regional clusters connected via Cloud VPN. The team wants to diagnose the issue and improve reliability. They suspect network round-trip time (RTT) is causing the latency, but they are not sure if the problem is at the application or network layer. Which tool should they use to pinpoint the exact cause?

Accepted Answer

Use Cloud Trace to analyze distributed traces and identify bottlenecks in request paths.. Cloud Trace is the correct tool because it provides end-to-end distributed tracing, which can capture the exact latency contribution of each gRPC call across microservices and regional clusters. By analyzing trace spans, the team can determine whether the increased latency is due to network round-trip time (RTT) between clusters or due to application-level processing delays within a service.

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Use Cloud Monitoring to view gRPC latency distributions and break down by service and method.

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Use VPC Flow Logs to examine network throughput and packet loss.

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Use Cloud Logging to search for error logs in the application containers.