PCDOE Practice Test 9 — 10 Questions

Question 1

Which THREE are benefits of migrating from Jenkins to Cloud Build?

Accepted Answer

No server maintenance. Cloud Build is a fully managed CI/CD service that runs on Google Cloud's infrastructure, eliminating the need for users to provision, configure, or maintain build servers. Unlike Jenkins, which requires ongoing administration of the Jenkins master and agent nodes, Cloud Build automatically scales resources and handles underlying server maintenance, including OS patches and hardware failures.

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Support for any VCS

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Built-in security scanning

Question 2

A team uses Cloud Build with a trigger on Cloud Source Repository. The build fails intermittently with error 'Failed to pull builder image 'gcr.io/cloud-builders/gcloud'' but sometimes succeeds. What is the most likely cause?

Accepted Answer

Network egress from Cloud Build is throttled due to high concurrency.. The intermittent failure to pull the builder image 'gcr.io/cloud-builders/gcloud' indicates a transient network issue rather than a permanent misconfiguration. Cloud Build uses a shared pool of network resources, and under high concurrency, egress traffic to Container Registry can be throttled, causing pull operations to time out or fail. This explains why the build sometimes succeeds and sometimes fails, as throttling depends on the current load.

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The Cloud Build worker pool is in a different region.

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The builder image is too large.

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The build service account lacks permissions to access Container Registry.

Question 3

You manage a production environment with a web service deployed on Compute Engine instances behind a HTTP(S) Load Balancer. The service has a health check configured on the load balancer, probing a health endpoint every 10 seconds. After a recent configuration change, you observe that all instances are marked as unhealthy and traffic is failing. The health check response is 200 OK from the instances, but the load balancer still marks them unhealthy. The health check configuration: protocol: HTTP, port: 80, request path: /health, interval: 10s, timeout: 5s, unhealthy threshold: 2. The instances are running a custom web server. What is the most likely cause?

Accepted Answer

The health check response is coming from a different server (e.g., a reverse proxy) that returns 200 but does not represent the actual service health.. The health check is receiving a 200 OK response from a reverse proxy or intermediary that is not the actual web server, so the load balancer's health check does not reflect the true health of the application. Even though the response is successful, the load balancer marks instances unhealthy because the health check is likely failing to reach the intended health endpoint on the custom web server, or the response is not coming from the service itself. This scenario often occurs when a reverse proxy (e.g., nginx) returns a static 200 for /health without forwarding the request to the backend service, causing the load balancer to see a healthy response but the service to be actually unhealthy.

Answer

The load balancer is misconfigured with an incorrect protocol (HTTPS instead of HTTP).

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The health check port is incorrectly configured as 80 but the service is listening on 8080.

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The health check timeout is greater than the interval, causing overlapping probes.

Question 4

Refer to the exhibit. You are investigating a performance issue where the api-server container is using excessive CPU. You run a Cloud Monitoring API query and receive the JSON configuration shown. However, the query returns no data points. What is the most likely cause?

Accepted Answer

The filter is missing required resource labels such as project_id, location, cluster_name, and namespace_name, causing no time series to match.. The Cloud Monitoring API query for the 'kubernetes.io/container/cpu/core_usage_time' metric type requires mandatory resource labels—specifically 'project_id', 'location', 'cluster_name', and 'namespace_name'—to uniquely identify the time series for a GKE container. Without these labels in the filter, the query cannot match any time series, resulting in no data points returned, even if the metric is actively emitting data.

Answer

The time interval specified is too short and falls outside the data retention period.

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The metric type 'kubernetes.io/container/cpu/core_usage_time' is deprecated and no longer available.

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The aggregation perSeriesAligner 'ALIGN_MEAN' is incompatible with the metric type, which requires 'ALIGN_RATE'.

Question 5

You are a DevOps engineer at a large e-commerce company that runs its production workloads on Google Kubernetes Engine (GKE) in the us-central1 region. The cluster has 500 nodes, each with 8 vCPUs and 32 GB of memory, and uses preemptible VMs for cost savings. Over the past month, the monthly GKE cost has increased by 30% unexpectedly. Upon reviewing the billing reports, you notice a significant spike in Compute Engine costs, specifically for 'Sustained Use Discount' line items, but the total cost is higher than expected. You also observe that the cluster's node utilization is inconsistent, with some nodes running at 90% CPU and memory while others are below 20%. Your team has been deploying stateless microservices and using Cluster Autoscaler with default settings. The application traffic is variable but predictable, with peaks on weekends. You need to reduce the GKE costs without impacting performance. What should you do?

Accepted Answer

Enable node auto-provisioning and migrate baseline workloads to nodes covered by committed use discounts (1-year or 3-year).. Enabling node auto-provisioning allows GKE to automatically select the most cost-effective node configurations for your workloads, reducing waste from over-provisioned nodes. Migrating baseline workloads to nodes covered by committed use discounts (CUDs) locks in lower prices for predictable usage, directly addressing the 30% cost spike caused by inconsistent utilization and unexpected sustained use discount charges. This combination optimizes both variable and steady-state workloads without sacrificing performance.

Answer

Increase the minimum number of nodes in the cluster to 300 and use a larger machine type to reduce the number of pods per node.

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Switch the cluster to a single-zone configuration and reduce the number of nodes to 200 to lower base costs.

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Reduce the number of preemptible VMs to 30% and use only on-demand VMs for the remaining nodes to improve reliability.

Question 6

You are a DevOps engineer at a media streaming company. Your application runs on Google Kubernetes Engine (GKE) and serves video content to users worldwide. The application uses a microservices architecture with a frontend service that handles user requests and a backend transcoding service that converts video files. Recently, you noticed that the transcoding service is causing performance bottlenecks during peak hours, leading to increased latency for users. You have enabled Cloud Monitoring and Cloud Trace and observed that the transcoding service's CPU utilization is consistently above 90% during peak times, and the queue of video transcoding tasks is growing. The current deployment has 5 replicas of the transcoding service with no autoscaling. You need to optimize the performance of the transcoding service to reduce latency. Your company has a limited budget and wants to minimize costs. What should you do?

Accepted Answer

Refactor the frontend to push transcoding tasks to a Cloud Pub/Sub topic, and create a separate deployment of workers that subscribe to the topic and perform transcoding. Configure HPA on the worker deployment based on the Pub/Sub subscription backlog.. It decouples the transcoding workload from user-facing requests using Cloud Pub/Sub, allowing the worker deployment to scale independently based on the backlog of tasks. This pattern reduces latency by preventing the frontend from being blocked by transcoding, and HPA on Pub/Sub backlog ensures cost-efficient scaling only when demand increases, aligning with the limited budget.

Answer

Enable Horizontal Pod Autoscaling (HPA) on the transcoding service based on CPU utilization, targeting 70% utilization.

Answer

Upgrade the transcoding service to a larger machine type with more CPU and memory.

Answer

Increase the number of replicas of the transcoding service to 10 and keep it static.

Question 7

A company runs a microservices architecture on GKE with Istio. They want to generate custom request-level metrics for SLO tracking without modifying application code. Which approach is most efficient?

Accepted Answer

Set up Istio telemetry with the Cloud Monitoring adapter. Istio's telemetry v2 can be configured to export custom request-level metrics (e.g., latency, error rate) to Cloud Monitoring via the Cloud Monitoring adapter without modifying application code. This approach leverages Istio's sidecar proxy to capture metrics at the service mesh layer, making it the most efficient and non-invasive method for SLO tracking.

Answer

Write a custom Prometheus exporter deploying in each pod

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Use the Cloud Monitoring agent to scrape metrics from pods

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Enable Stackdriver for GKE (deprecated)

Question 8

You want to send alerts to a Slack channel when a critical error occurs. What should you do?

Accepted Answer

Configure a Slack webhook notification channel in Cloud Monitoring and associate it with an alerting policy. Cloud Monitoring alerting policies can directly send notifications to Slack via a configured webhook notification channel. When a critical error triggers the alerting policy, Cloud Monitoring sends an HTTP POST request to the Slack webhook URL, delivering the alert message to the specified Slack channel. This is the native, event-driven approach without requiring custom code or intermediate services.

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Set up a webhook in Cloud Logging and point it to Slack

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Use Cloud Tasks to schedule a job that queries logs and sends Slack messages

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Create a Cloud Logging export to Pub/Sub and subscribe using a Cloud Function that sends to Slack

Question 9

Which THREE of the following are valid considerations when using organization policies to enforce compliance in a DevOps environment?

Accepted Answer

Child-level organization policies can only add more restrictions, not remove restrictions set by a parent.. Organization policies follow a hierarchical inheritance model where child policies can only impose additional restrictions (i.e., they are more restrictive) than the parent policy. This is enforced by the Google Cloud Resource Manager hierarchy, ensuring that a policy set at the organization level cannot be relaxed by a folder or project policy.

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An organization policy must be enabled by a service agent before it can be used.

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IAM conditions can be used to override an organization policy for specific resources.

Question 10

Your company runs a multi-region application on GKE across us-east1 and europe-west1. The application serves a global user base with a strict SLO of 99.95% availability. Recently, the team noticed that during peak hours, some users in South America experience high latency and intermittent errors. The GKE clusters are monitored via Cloud Monitoring with custom dashboards and alerting policies. The team has set up a single alerting policy that triggers when the global error rate exceeds 0.1%. However, the alert fires only after the issue has persisted for 10 minutes, and by then the customer impact is already significant. You need to improve the detection and response time. Which action should you take first?

Accepted Answer

Create separate alerting policies per region with shorter evaluation periods, and set up a notification channel for the on-call team.. The current single global alerting policy with a 10-minute evaluation period introduces a significant delay in detecting regional issues. By creating separate alerting policies per region with shorter evaluation periods, you can detect and respond to regional anomalies (like high latency in South America) much faster, directly improving the detection time for the multi-region application and reducing customer impact.

Answer

Add a dashboard that shows latency by region and set up a log-based metric for error counting.

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Reduce the alerting policy's duration to 1 minute and increase the threshold to 0.5% to reduce noise.

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Implement a canary deployment strategy to roll back changes quickly.