PCDOE Practice Test 4 — 10 Questions

Question 1

A company uses Compute Engine with committed use discounts for 1-year. They need to reduce costs further. What should they do?

Accepted Answer

Rightsize their VMs based on recommender recommendations.. Rightsizing VMs based on Google Cloud's Recommender recommendations is the correct next step because committed use discounts (CUDs) lock in a discount on a committed spend or resource amount, but if the VMs are over-provisioned, you are still paying for unused capacity. Rightsizing reduces the baseline resource consumption, which lowers both on-demand and committed costs. This is the standard FinOps practice after securing a discount.

Answer

Use sustained use discounts instead.

Answer

Use preemptible VMs for all workloads.

Answer

Increase committed use discount term to 3 years.

Question 2

During a Cloud Build pipeline, a build step fails because the Docker image tag already exists in Container Registry. The team wants to avoid overwriting tags. What is the best practice to resolve this?

Accepted Answer

Use the commit SHA as the image tag in the build step.. Using the commit SHA as the image tag guarantees uniqueness because each commit produces a distinct SHA. This prevents tag collisions in Container Registry without overwriting, as the SHA is immutable for that commit. It also provides traceability back to the exact source code version that produced the image.

Answer

Specify the :latest tag and always push to that tag.

Answer

Add a step to pull the image before building to ensure it's present.

Answer

Configure the build to retry on failure with a backoff.

Question 3

A GKE cluster node fails, causing pods to be rescheduled. However, some pods remain in 'CrashLoopBackOff' state. After examining logs, you find the application has a dependency on local SSD that was ephemeral. What is the best long-term solution?

Accepted Answer

Use PersistentVolumes with ReadWriteOnce access mode.. The application's dependency on local SSD (ephemeral storage) means that when the node fails, the data is lost, causing the pods to crash. PersistentVolumes (PVs) with ReadWriteOnce (RWO) access mode provide durable, node-independent storage that survives node failures, ensuring pods can be rescheduled on any node and access their data. This is the best long-term solution because it decouples storage from the node lifecycle, preventing CrashLoopBackOff due to missing local data.

Answer

Configure pod anti-affinity.

Answer

Increase the node pool size.

Answer

Use a DaemonSet to run the application.

Question 4

Which Cloud Run setting controls the maximum number of requests a container can handle concurrently?

Accepted Answer

concurrency. The `concurrency` setting in Cloud Run defines the maximum number of simultaneous requests that a single container instance can process at any given time. This directly controls how many requests are routed to a single container before Cloud Run spins up additional instances, optimizing resource utilization and preventing overload.

Answer

timeout

Answer

max-instances

Answer

min-instances

Question 5

An SRE team created the above logs-based metric. They expect it to count the number of HTTP 500 errors per instance. However, the metric shows no data. What is the most likely cause?

Accepted Answer

The log entries might not have the 'status' field in jsonPayload; it could be in a different location or format.. The most likely reason for a logs-based metric showing no data is that the log entries do not contain the expected 'status' field in jsonPayload, or it is located in a different field (e.g., httpRequest.status) or formatted as a string instead of an integer. Cloud Logging metrics rely on exact field paths defined in the metric descriptor; if the field is missing or misnamed, no data points are generated.

Answer

The metric kind is DELTA but should be CUMULATIVE.

Answer

The metric name does not follow the required naming convention.

Answer

The labelExtractors must use regex instead of JSON path.

Question 6

You are the DevOps engineer for a large gaming company. Your game backend runs on Compute Engine instances behind a global HTTP(S) Load Balancer. You have set up Cloud Monitoring with an uptime check for the load balancer's IP address, and you are using logging to capture 404 errors. Recently, a new game update caused a surge in traffic, and you started receiving many alerts from your uptime check indicating that the site is down. However, you verify that the backend instances are healthy and the load balancer is responding correctly, though some requests are timing out due to the increased load. Your alerting policy currently triggers when 2 consecutive checks fail. What is the most likely reason for the false positive alerts?

Accepted Answer

The uptime check's timeout is too short for the current response times.. The uptime check's timeout is too short for the current response times. When a surge in traffic causes some requests to time out, the load balancer may still respond correctly to most requests, but the uptime check—which has a fixed timeout (default 10 seconds)—fails if the response does not arrive within that window. Since the alert triggers after 2 consecutive failures, the check falsely reports the site as down even though the backend and load balancer are healthy.

Answer

The global load balancer's health check is failing due to the surge.

Answer

The monitoring project has reached its limit for concurrent uptime checks.

Answer

The uptime check is configured to check a specific URL that is returning a 503 status code.

Question 7

A company runs a high-traffic web application on GKE. Which three practices can help optimize performance under load? (Select THREE.)

Accepted Answer

Use a global load balancer. Option A (Use a global load balancer) is correct because a Google Cloud global external load balancer distributes traffic across multiple regions using anycast IP and Google's global edge network, reducing latency and absorbing load spikes for a high-traffic web application on GKE. Option C (Store session state in Cloud Memorystore) is correct because externalizing session state to a managed Redis/Memcached service makes pods stateless, allowing horizontal scaling and even distribution of requests without session affinity bottlenecks. Option D (Enable HorizontalPodAutoscaler with custom metrics) is correct because HPA with custom metrics (e.g., via Stackdriver/Cloud Monitoring adapters) scales pod replicas based on application-level signals like requests per second or queue depth, matching capacity to real load rather than just CPU. Option B (Use a multi-cluster ingress) is not among the correct answers because it addresses multi-cluster traffic routing and failover rather than directly optimizing performance under load for a single GKE workload. Option E (Increase the number of nodes to maximum) is not among the correct answers because blindly maximizing node count is costly and inefficient without autoscaling policies tied to actual demand.

Answer

Use a multi-cluster ingress

Answer

Increase the number of nodes to maximum

Question 8

A data processing company runs nightly batch jobs that can tolerate interruptions. They want to minimize compute costs. Which compute option should they use?

Accepted Answer

Preemptible VMs. Preemptible VMs are the correct choice because they offer the lowest cost for interruptible workloads. They are priced significantly lower than standard VMs (up to 80% discount) but can be terminated by Google Cloud at any time, with a 30-second warning, and have a maximum lifetime of 24 hours. Since the batch jobs can tolerate interruptions, preemptible VMs align perfectly with the cost-minimization goal.

Answer

VMs with committed use discounts

Answer

Standard VMs with sustained use discounts

Answer

Sole-tenant nodes

Question 9

A team implements canary deployments using Cloud Deploy and deploys to GKE. They want to automatically roll back if the canary release's error rate exceeds 5% within 10 minutes. Which approach should they use?

Accepted Answer

Set up Cloud Deploy with a rollout strategy that uses Cloud Monitoring metrics to automatically rollback.. Cloud Deploy supports rollout strategies that can reference Cloud Monitoring metrics to automatically roll back a canary release if error rates exceed a threshold. This native integration allows defining a canary deployment with automated rollback based on metrics like error rate within a specified time window, directly meeting the requirement.

Answer

Deploy using Spinnaker on GKE with a canary pipeline that includes an automated rollback step.

Answer

Configure a Cloud Build step to monitor the canary and rollback if needed.

Answer

Use a GKE rolling update strategy within the deployment manifest.

Question 10

In Google's incident management process, which role is responsible for communication with stakeholders and users during an incident?

Accepted Answer

Communications Lead.. In Google's incident management process, the Communications Lead is explicitly responsible for managing all external and internal communications, including updates to stakeholders and users. This role ensures that accurate, timely information is disseminated while the Incident Commander focuses on coordinating the response. The Communications Lead does not engage in technical troubleshooting or operational tasks, which are handled by other roles.

Answer

Incident Commander.

Answer

Technical Lead.

Answer

Operations Lead.