PCDOE Practice Test 12 — 15 Questions

Question 1

A team uses Spanner for a global database. They notice increased read latency and high CPU utilization on some nodes. The workload is read-heavy with occasional writes. Which action is most likely to improve performance?

Accepted Answer

Add more nodes to the instance and ensure read requests are distributed evenly.. In a read-heavy Spanner workload with high CPU utilization on some nodes, adding more nodes and ensuring read requests are distributed evenly (Option D) directly addresses the bottleneck by increasing the instance's compute capacity and spreading the load across all nodes. Spanner's architecture uses a shared-nothing design where each node handles a portion of the data and traffic; uneven distribution can cause hot spots. Adding nodes scales out processing power, and ensuring even distribution (e.g., via proper key design or using Spanner's built-in load balancing) reduces latency and CPU spikes on individual nodes.

Answer

Create read-only replicas in each region.

Answer

Split the most frequently read tables into smaller tables.

Answer

Increase the number of nodes in the instance.

Question 2

A DevOps engineer is optimizing a Cloud Run service that experiences cold starts. The service is written in Python and uses several large libraries. Which change is most effective to reduce cold start latency?

Accepted Answer

Set a minimum number of instances to keep containers warm.. Setting a minimum number of instances (option B) ensures that a baseline of container instances is always warm and ready to serve requests, eliminating cold starts for those instances. Cold starts occur when a new container must be initialized, including loading large Python libraries, which adds significant latency. By keeping a minimum number of instances running, the service avoids the initialization delay for the first request to each instance.

Answer

Increase the maximum number of concurrent requests per container.

Answer

Set a longer request timeout.

Answer

Increase the CPU allocation for the service.

Question 3

Your team deploys a microservice on Google Kubernetes Engine (GKE) that serves an API with low latency requirements. Users report that the API occasionally times out during peak hours. You check the GKE metrics and see that CPU utilization is below 50% but memory is near 100% on the nodes. What is the most likely cause and what should you do?

Accepted Answer

The application is memory-constrained; increase memory resource limits for the pod.. Option B is correct because the symptoms—low CPU utilization but near 100% memory usage on nodes, with API timeouts during peak hours—indicate that the application is hitting memory limits. When a pod exceeds its memory request, the kernel can OOM-kill it, causing request failures and timeouts. Increasing the memory resource limits for the pod allows it to allocate more heap or cache, preventing OOM kills and stabilizing latency.

Answer

The nodes are under-provisioned; add more nodes to the cluster.

Answer

The application is CPU-bound; increase CPU resource limits for the pod.

Answer

The network bandwidth is insufficient; increase the machine type for nodes.

Question 4

A service experiences increased latency and HTTP 503 errors. The engineer finds that the backend managed instance group (MIG) is at max instances and CPU utilization is 90%. Which TWO actions should the engineer take to restore the service quickly?

Accepted Answer

Enable autoscaling based on HTTP load balancing utilization. Option A is correct because enabling autoscaling based on HTTP load balancing utilization allows the MIG to scale out based on the actual request load, which directly addresses the 503 errors caused by the backend being at max capacity. This metric is more responsive to traffic spikes than CPU utilization alone, as it reflects the frontend load balancer's view of backend capacity.

Answer

Increase the autoscaling target CPU utilization to 95%

Answer

Reduce the autoscaling target CPU utilization to 50%

Answer

Reduce the number of instances to avoid resource contention

Question 5

A company's SRE team is designing an incident management process. They want to ensure that alerts are actionable and that on-call engineers are not overwhelmed by false positives. Which approach should they take?

Accepted Answer

Define SLOs and set alert thresholds based on historical error budget consumption. Option D is correct because defining SLOs and setting alert thresholds based on historical error budget consumption ensures alerts are directly tied to user-facing reliability. This approach prevents false positives by only triggering when the error budget is being consumed faster than expected, making alerts actionable and reducing noise for on-call engineers.

Answer

Use only critical severity alerts and rely on manual dashboard review for lower severity

Answer

Create alerting policies for every available metric to ensure nothing is missed

Answer

Set all alert thresholds to 50% above the average value to avoid false positives

Question 6

A team uses Google Kubernetes Engine (GKE) with cluster telemetry enabled. During an incident, they notice that a deployment's pods are repeatedly crashing with Exit Code 137. The team wants to investigate the root cause. Which two Google Cloud services should they use together to correlate resource usage and logs?

Accepted Answer

Cloud Monitoring and Cloud Logging. Exit Code 137 indicates that a container was killed by SIGKILL (signal 9), typically due to an out-of-memory (OOM) condition. Cloud Monitoring provides metrics such as memory usage and OOM kill counts, while Cloud Logging captures the container's termination logs and system events. By correlating these two services, the team can identify when memory usage spiked and confirm that the pod was OOM-killed, enabling root cause analysis.

Answer

Security Command Center and Cloud Logging

Answer

Cloud Error Reporting and Cloud Logging

Question 7

A company is using BigQuery for analytics and wants to control costs. They have many queries that scan large amounts of data. Which approach is most effective in reducing query costs?

Accepted Answer

Partition tables by date and use partition pruning in queries.. Partitioning tables by date and using partition pruning in queries directly reduces the amount of data scanned by BigQuery, which is the primary driver of on-demand query costs. By filtering on the partition column, BigQuery can skip entire partitions that do not match the query criteria, minimizing the bytes processed. This is the most effective cost-control measure because it addresses the root cause of high costs—excessive data scanning—without requiring a pricing model change or additional resource commitments.

Answer

Switch to flat-rate pricing to cap costs.

Answer

Reserve BigQuery slots for dedicated capacity.

Answer

Use clustering to organize data within partitions.

Question 8

You are the DevOps engineer for a large e-commerce platform running on Google Kubernetes Engine (GKE). During a flash sale, you observe that the payments service is experiencing high latency and intermittent errors. The service is deployed with HorizontalPodAutoscaler (HPA) based on CPU utilization. You need to quickly diagnose and mitigate the issue. Which TWO actions should you take?

Accepted Answer

Use Cloud Monitoring to examine the payments service's request latency and error rate metrics, and create a custom dashboard for real-time monitoring.. Option A is correct because Cloud Monitoring provides the necessary observability to diagnose the root cause of high latency and intermittent errors by examining request latency and error rate metrics. Creating a custom dashboard enables real-time monitoring, allowing you to correlate performance degradation with traffic spikes during the flash sale. This is the first step in incident management: observe before acting.

Answer

Check the GKE node's network performance using VPC Flow Logs and increase the node pool size.

Answer

Modify the HPA to use memory utilization instead of CPU, as memory is more indicative of the service's performance.

Answer

Configure a custom metric in Cloud Monitoring for the payments service's request queue depth and use it for HPA.

Question 9

A company uses Cloud Run for a critical service and needs to set up alerting for 5xx errors. They want to receive a notification within 1 minute of the error rate exceeding 1% for any 1-minute window. Which alerting approach should they use?

Accepted Answer

Create a Cloud Monitoring alerting policy using the 'Request count' metric with a condition that compares the ratio of 5xx responses to total requests over a 1-minute window.. Option D is correct because Cloud Monitoring's alerting policies can directly compute the ratio of 5xx responses to total requests using the 'Request count' metric with a custom ratio condition over a 1-minute window. This approach meets the 1-minute notification latency requirement without additional infrastructure, as Cloud Monitoring evaluates the metric every 60 seconds and triggers alerts based on the sliding window.

Answer

Set up a log-based metric for 5xx responses and create an alert on the metric.

Answer

Create a Cloud Logging sink to a Pub/Sub topic and trigger a Cloud Function that sends notifications.

Answer

Use Cloud Monitoring's log-based alerting to trigger on every 5xx log entry.

Question 10

Refer to the exhibit. The output shows a recommendation from the Cloud Cost Optimization recommender for an instance in us-central1-a. The instance is a production web server that consistently runs at 25% CPU utilization during peak hours. What should the DevOps engineer do to implement this recommendation with minimal risk?

Accepted Answer

Stop the instance, change the machine type to n2-standard-4, and start it again.. Option A is correct because the Cloud Cost Optimization recommender has identified that the current instance is over-provisioned for its actual workload (25% CPU utilization during peak hours). By stopping the instance, changing the machine type to n2-standard-4 (which has 4 vCPUs and 16 GB RAM, matching the recommended 4 vCPUs and 15 GB memory), and starting it again, the engineer rightsizes the instance without incurring downtime during the change itself. This approach minimizes risk because the instance is stopped only briefly for the machine type change, and the new machine type is a standard, well-tested configuration that aligns with the recommender's analysis.

Answer

Ignore the recommendation because the instance is production and any change might cause downtime.

Answer

Add a second n2-standard-4 instance behind a load balancer to distribute load.

Answer

Use a custom machine type with 4 vCPUs and 32 GB memory to ensure enough RAM.

Question 11

Refer to the exhibit. An App Engine application returns 504 errors. The application calls an external API and processes the result. Which change is most likely to resolve the errors?

Accepted Answer

Increase the App Engine request timeout in app.yaml to 120 seconds.. A 504 error from App Engine indicates the request exceeded the timeout limit before the application could respond. The default App Engine request timeout is 60 seconds, and since the application calls an external API and processes the result, the total time may exceed this limit. Increasing the request timeout in app.yaml to 120 seconds allows the application more time to complete the external API call and processing, resolving the 504 error.

Answer

Reduce the idle timeout in the scaling settings.

Answer

Change the scaling type from automatic to manual.

Answer

Increase the number of instances to handle the load.

Question 12

Your organization is adopting DevOps practices and needs to bootstrap a Google Cloud organization with multiple projects. You want to enforce consistent resource naming conventions and apply common organization policies across all projects. Which two services should you use together to achieve this?

Accepted Answer

Service Accounts and IAM roles. Organization Policies allow you to centrally constrain actions across all projects in the hierarchy, while Resource Manager folders let you group projects and apply policies consistently. Together, they enable you to enforce naming conventions (e.g., via a custom constraint) and common policies (e.g., disabling external IPs) across multiple projects without manual per-project configuration.

Answer

Cloud Shell and Cloud Source Repositories

Answer

Cloud Deployment Manager and Cloud Audit Logs

Question 13

You are bootstrapping a Google Cloud organization. You need to set up a hierarchical structure that allows you to apply policies to groups of projects based on their environment (e.g., development, staging, production). What is the recommended way to organize resources?

Accepted Answer

Create folders under the organization for each environment and place projects in the appropriate folder.. Option B is correct because Google Cloud's resource hierarchy (Organization -> Folders -> Projects) is specifically designed to group projects by environment and apply consistent policies (e.g., IAM, organization policies) at the folder level. By creating folders for development, staging, and production, you can enforce environment-specific controls (like VPC Service Controls or resource location restrictions) without duplicating policies per project.

Answer

Use resource tags to label projects by environment and apply policies via tag-based conditions.

Answer

Create separate organizations for each environment.

Answer

Use labels on projects to identify environments and then use Cloud Asset Inventory to enforce policies.

Question 14

Which THREE actions should be taken to ensure compliance with the principle of least privilege when bootstrapping a Google Cloud organization? (Choose 3)

Accepted Answer

Use service accounts for automated processes and grant them the minimum required roles.. Option A is correct because service accounts are the recommended identity for automated processes in Google Cloud, and granting them only the minimum required roles directly implements the principle of least privilege. This prevents over-permissioning and reduces the attack surface for automated workflows.

Answer

Assign the Owner role at the organization level to a small group of administrators.

Answer

Use primitive roles (Owner, Editor, Viewer) to simplify management.

Question 15

A company is bootstrapping their Google Cloud organization for DevOps. They want to implement a least-privilege model for service accounts used by CI/CD pipelines. The pipelines need to deploy resources in multiple projects. What is the best practice for managing service account keys?

Accepted Answer

Use workload identity federation to allow the CI/CD system to impersonate a service account without keys.. Option D is correct because workload identity federation allows an external CI/CD system (e.g., Jenkins, GitHub Actions) to impersonate a Google Cloud service account without managing or storing any long-lived keys. This eliminates the security risk of key leakage and aligns with the least-privilege principle by enabling short-lived, scoped credentials via the Security Token Service (STS) and OAuth 2.0 token exchange.

Answer

Use a user account for the CI/CD pipeline and assign it the necessary roles.

Answer

Store service account keys in Secret Manager and have the pipeline retrieve them at runtime.

Answer

Generate a single service account key and securely distribute it to the CI/CD system.