PCDOE Practice Test 10 — 10 Questions

Question 1

A company is bootstrapping a Google Cloud organization for DevOps. Which TWO practices should be implemented to ensure secure and efficient management of infrastructure as code (IaC) pipelines?

Accepted Answer

Use a dedicated project for CI/CD pipelines that houses Cloud Build triggers and Cloud Source Repositories.. Option B is correct because a dedicated CI/CD project isolates build infrastructure, Cloud Build triggers, and Cloud Source Repositories from workload projects, limiting the blast radius of compromised pipeline credentials and enabling centralized audit logging and IAM control over the deployment toolchain. Option D is correct because least-privilege service accounts scoped per environment (for example, distinct Terraform service accounts for dev, staging, and prod) enforce separation of duties and prevent human users from bypassing IaC by making direct production changes, which is a core Google Cloud security best practice. Option A is wrong because storing API keys in Terraform configuration files exposes secrets in version control and state files; secrets should be kept in Secret Manager and referenced dynamically. Option C is wrong because a single project collapses IAM, quota, and billing boundaries across environments, so a misconfiguration or compromised credential can affect production. Option E is wrong because requiring security-team approval for every change creates a bottleneck that undermines DevOps efficiency; approval gates should be risk-based and automated where possible.

Answer

Store infrastructure secrets (e.g., API keys) directly in Terraform configuration files for simplicity.

Answer

Use a single project to host all development, staging, and production environments to reduce complexity.

Answer

Require manual approval from a security team for every infrastructure change.

Question 2

Your team is using Cloud Monitoring to track the health of a distributed microservices application. You notice that the error rate for the checkout service has increased significantly, but no alerts are firing. The SLO for checkout is 99.9% availability over a 28-day rolling window. You inspect the alerting policy and find it uses a time series aggregation with a 1-minute alignment period and a condition that triggers when the ratio of errors to total requests exceeds 0.001 for 5 consecutive minutes. What is the most likely reason the alert is not firing?

Accepted Answer

The alert condition requires 5 consecutive minutes of breach, but the error rate spikes are intermittent and not sustained.. The alert condition requires the error ratio to exceed 0.001 for 5 consecutive 1-minute alignment periods. If the error rate spikes are intermittent—lasting only a minute or two before returning to normal—the condition of 5 consecutive minutes of breach is never met, so the alert remains silent. This is a classic case where the alerting policy's duration setting is too long relative to the bursty nature of the errors.

Answer

The error budget has been exhausted, so the alert is suppressed.

Answer

The SLO window is too long, and the alert condition uses a different measurement period.

Answer

The ratio threshold is too high because the total request count is low.

Question 3

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. 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 4

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.. 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 5

You are investigating a slow increase in latency for a service running on Compute Engine. You have Cloud Monitoring and Cloud Logging set up. Which tool would best help you identify the cause of the latency?

Accepted Answer

Cloud Trace. Cloud Trace is designed to capture latency data from distributed applications by tracing requests as they propagate through services. It provides detailed latency distributions and per-span breakdowns, allowing you to pinpoint which component or operation is causing the slowdown. For a Compute Engine service with Cloud Monitoring and Logging already in place, Cloud Trace is the most direct tool to analyze request-level latency.

Answer

Error Reporting

Answer

Cloud Profiler

Answer

Cloud Debugger

Question 6

Which TWO of the following are best practices for implementing service monitoring in Google Cloud? (Choose 2)

Accepted Answer

Use Cloud Monitoring uptime checks to verify that services are reachable from external locations.. Option B is correct because Cloud Monitoring uptime checks are designed to probe HTTP, HTTPS, or TCP endpoints from multiple external locations, confirming that a service is reachable from the user's perspective and enabling alerting on availability. Option D is correct because the RED method (Rate, Errors, Duration) is a recognized best practice for defining SLIs for request-driven services, directly measuring user-perceived behavior such as request throughput, failure rate, and latency. Option A is incorrect because static thresholds ignore historical baselines and seasonality, leading to noisy or missed alerts; dynamic, baseline-aware thresholds are preferred. Option C is incorrect because the USE method (Utilization, Saturation, Errors) is intended for resource-level monitoring (e.g., CPU, memory, disk), not service-level monitoring, where RED is the appropriate framework. Option E is incorrect because best practice is to alert on symptom-based metrics that reflect user impact (e.g., latency, error rate) rather than cause-based metrics like CPU utilization, which may not correlate with actual service degradation.

Answer

Set static alert thresholds without considering historical baselines.

Answer

Use the USE method (Utilization, Saturation, Errors) for service-level monitoring.

Answer

Alert on cause-based metrics (e.g., CPU utilization) rather than symptom-based metrics (e.g., latency).

Question 7

Your team is running a high-traffic web application on Google Kubernetes Engine (GKE) and has configured Horizontal Pod Autoscaling (HPA) based on CPU utilization. Recently, the application experienced intermittent latency spikes during traffic bursts. You suspect that the HPA is not scaling quickly enough. Which TWO actions would most effectively improve the autoscaling responsiveness?

Accepted Answer

Configure a custom metric in HPA based on the application's request latency (e.g., p99 latency).. Using a custom metric based on request latency (e.g., p99 latency) allows HPA to react to application performance degradation directly, rather than relying solely on CPU utilization which may lag behind traffic bursts. This provides a more immediate signal for scaling, as latency spikes often precede CPU saturation in web applications. Option D is correct because reducing the --horizontal-pod-autoscaler-upscale-stabilization window (default 5 minutes) to a lower value like 30 seconds decreases the time HPA waits before acting on scale-up recommendations, enabling faster response to sudden load increases.

Answer

Increase the target CPU utilization percentage to 90% so that pods are less likely to be added.

Answer

Set the maxReplicas of the HPA to a value lower than the expected peak traffic to force faster scaling.

Answer

Increase the --horizontal-pod-autoscaler-sync-period flag to 30 seconds and increase the --horizontal-pod-autoscaler-upscale-delay flag to 5 minutes.

Question 8

A startup wants to implement infrastructure as code for their Google Cloud environment to ensure reproducibility. They are using Terraform and want to manage state securely. What is the recommended approach?

Accepted Answer

Store Terraform state in a Cloud Storage bucket with object versioning enabled.. Storing Terraform state in a Cloud Storage bucket with object versioning enabled is the recommended approach because it provides a centralized, remote backend that supports state locking (via Cloud Storage's object lease mechanism) and version history for rollback. This ensures consistency across team members and protects against state corruption or accidental deletion, aligning with infrastructure as code best practices for reproducibility.

Answer

Store Terraform state in a file in Cloud Shell home directory.

Answer

Store Terraform state in Cloud Source Repositories as a YAML file.

Answer

Store Terraform state locally on each developer's machine and use git to sync.

Question 9

A DevOps engineer needs to verify if a load balancer's health check is behaving normally by examining historical trends. Where should they look?

Accepted Answer

Cloud Monitoring Metrics Explorer. Cloud Monitoring Metrics Explorer is the correct place to examine historical trends of a load balancer's health check because it provides time-series metrics such as `https/backend_request_count`, `https/backend_latencies`, and health check probe success/failure counts. These metrics can be queried over custom time ranges and aggregated to detect anomalies or degradation patterns, which is exactly what the question asks for — verifying historical trends of health check behavior.

Answer

Cloud Logging

Answer

Cloud Console health check page

Answer

Cloud Load Balancing logs

Question 10

A company wants to implement a DevOps culture in their new Google Cloud organization. Which THREE practices align with Google's DevOps principles? (Choose three.)

Accepted Answer

Monitor systems with telemetry and logs.. Option A is correct because Google's DevOps principles emphasize observability: collecting telemetry (metrics, traces) and logs lets teams detect issues, measure service health, and make data-driven decisions, which is foundational to SRE and DevOps practices. Option B is correct because continuous integration and continuous delivery (CI/CD) automate build, test, and release pipelines, enabling small, frequent, low-risk changes that align with Google's DORA-based DevOps guidance. Option E is correct because blameless post-mortems are a core Google SRE practice: they focus on systemic causes and learning rather than punishing individuals, which fosters psychological safety and continuous improvement. Option C does not belong because a monolithic architecture is not a DevOps practice and can hinder independent deployability and scaling, whereas Google favors loosely coupled, service-oriented designs. Option D does not belong because centralizing all operations in a single team contradicts DevOps's shared responsibility and collaboration between development and operations, which Google promotes through practices like SRE and error budgets.

Answer

Use a monolithic architecture to simplify deployments.

Answer

Centralize all operations in a single team.