PCDOE Practice Test 2 — 30 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.

Question 11

Which THREE steps are typically part of a formal incident postmortem according to Google SRE best practices?

Accepted Answer

Assign action items with deadlines.. Option B is correct because a formal postmortem must produce concrete, owned action items with deadlines so that follow-up work is tracked and completed rather than left as vague intentions. Option C is correct because reconstructing a precise incident timeline — detection, escalation, mitigation, and resolution times — is a foundational step that lets the team reason about what happened and where response gaps existed. Option E is correct because Google SRE postmortems focus on identifying contributing factors and root causes in a blameless manner, examining the technical and organizational conditions that allowed the failure. Option A does not belong because postmortems are explicitly blameless and do not seek to name or blame an individual. Option D does not belong because immediate remediation is part of incident response and mitigation, not the postmortem, which occurs after the incident is resolved to analyze and learn from it.

Answer

Identify the person responsible for the incident.

Answer

Implement a solution immediately.

Question 12

A DevOps engineer notices that a Cloud Build trigger is not firing when commits are pushed to a Cloud Source Repositories repository. The trigger is configured with an invert regex for the branch filter. What could be the issue?

Accepted Answer

The branch name matches the exclude pattern; the trigger ignores matching branches.. When a Cloud Build trigger is configured with an invert regex for the branch filter, it means the trigger will fire only for branches that do NOT match the specified regex pattern. If the branch name matches the exclude pattern, the trigger ignores commits on that branch, which is why the trigger is not firing. This is the intended behavior of the invert_regex flag in Cloud Build triggers.

Answer

The repository is in a different region.

Answer

The commit was made by a service account.

Answer

The trigger's service account lacks read access to the repository.

Question 13

A DevOps team wants to implement a CI/CD pipeline for a microservices application deployed on Google Kubernetes Engine (GKE). They need to ensure that each service is built, tested, and deployed independently with minimal manual intervention. Which TWO practices should they implement?

Accepted Answer

Use Cloud Deploy to manage progressive delivery (e.g., canary, blue/green) to GKE clusters.. Option A is correct because Cloud Deploy is Google Cloud's managed continuous delivery service that natively supports progressive delivery strategies such as canary and blue/green deployments to GKE clusters, enabling each microservice to be released independently with automated rollout and rollback controls. Option D is correct because Cloud Build triggers can be configured (e.g., via branch or tag patterns, or included/excluded files) to fire on pull requests and run per-service build and test steps, giving each microservice its own independent CI workflow with minimal manual intervention. Option B is not required for this scenario: Cloud Source Repositories is a Git hosting option, but the question focuses on build/test/deploy independence, and Cloud Build triggers work with GitHub, Bitbucket, or GitLab as well, so it is not one of the two best practices. Option C is wrong because a monolithic repository with simultaneous deployment of all services directly contradicts the requirement for independent build, test, and deployment of each microservice. Option E is wrong because a single shared Cloud Build configuration with conditional steps couples all services into one pipeline, reducing independence and increasing the blast radius of changes rather than giving each service its own build and test flow.

Answer

Use Cloud Source Repositories integrated with Cloud Build for version control and triggering builds.

Answer

Use a monolithic repository and deploy all services simultaneously to ensure consistency.

Answer

Use a single Cloud Build configuration file for all services with conditional steps to handle different services.

Question 14

During a Cloud Build execution, a step fails due to timeout. What is the first thing to check?

Accepted Answer

Check the build logs for errors. When a Cloud Build step fails due to a timeout, the build logs are the first and most authoritative source of diagnostic information. They contain the exact error messages, exit codes, and step output that reveal why the step exceeded its timeout — for example, a hanging command, a missing dependency, or a resource contention issue. Checking logs before making any configuration changes ensures you address the root cause rather than treating symptoms.

Answer

Check network connectivity

Answer

Increase machine type for the build

Answer

Increase the timeout in cloudbuild.yaml

Question 15

Which service is commonly used for time-series data and real-time analytics?

Accepted Answer

Bigtable. Bigtable is a fully managed, scalable NoSQL database optimized for large analytical and operational workloads, including time-series data and real-time analytics. It provides sub-10ms latency for high-throughput reads and writes, making it ideal for IoT, monitoring, and financial data streams. Its column-oriented storage and automatic sharding support efficient time-based queries and aggregation.

Answer

Cloud SQL

Answer

Firestore

Answer

Cloud Spanner

Question 16

Your team wants to create a dashboard that shows request latency broken down by API version. Which approach is most efficient?

Accepted Answer

Use Cloud Monitoring Metrics Explorer to query the latency metric, group by API version, and save the chart as a dashboard. Cloud Monitoring Metrics Explorer allows you to directly query the latency metric (e.g., `request_latencies`) and group by the `version` label, then save the resulting chart as a dashboard widget. This is the most efficient approach because it requires no data export, no custom code, and no additional services — the metric is already available in Cloud Monitoring if your API is instrumented with the appropriate label.

Answer

Write a custom application to output metrics to a file and send to Cloud Monitoring

Answer

Export Cloud Logging logs to BigQuery and create a dashboard in Data Studio

Answer

Enable Cloud Trace and create a dashboard based on trace data

Question 17

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.

Answer

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

Answer

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

Answer

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

Question 18

An e-commerce platform uses Cloud Load Balancing with backend services running on Compute Engine managed instance groups. During Black Friday sales, the application experiences high latency and some 503 errors. The team uses autoscaling based on average CPU utilization, but scaling is too slow—Cloud Monitoring shows CPU rises to 90% before new instances are added. The team needs to reduce latency and eliminate 503 errors. What should they do?

Accepted Answer

Change the autoscaling metric to 'requests per second' and set a lower target value. Switching the autoscaling metric from average CPU utilization to 'requests per second' (RPS) with a lower target value allows the autoscaler to react more quickly to traffic spikes. CPU utilization is a lagging indicator that rises only after requests have already been queued and processed, whereas RPS directly reflects incoming load. By setting a lower target RPS, the autoscaler can add instances before the backend becomes saturated, reducing latency and eliminating 503 errors.

Answer

Use HTTP load balancing with a larger backend timeout

Answer

Enable Cloud CDN for all dynamic content

Answer

Increase the cooldown period for the autoscaling policy

Question 19

An engineer receives an alert that a service's error rate has exceeded the threshold. To investigate, which log-based metric should the engineer query in Cloud Logging to identify the root cause?

Accepted Answer

Error log count grouped by service name.. Error log count grouped by service name directly surfaces which specific service is generating the elevated error rate. In Cloud Logging, log-based metrics are user-defined counters extracted from log entries; querying the error log count per service isolates the offending component, enabling root cause identification without mixing in unrelated performance data.

Answer

Request latency histogram.

Answer

CPU utilization of the service instances.

Answer

Network bytes sent per instance.

Question 20

Refer to the exhibit. The output shows three folders created directly under the organization node. Which gcloud command was most likely executed to produce this output?

Accepted Answer

gcloud resource-manager folders list --organization=123456789012. The `gcloud resource-manager folders list --organization=123456789012` command lists all folders directly under the specified organization node. The output shows three folders (e.g., folder1, folder2, folder3) at the top level, which matches the behavior of listing folders with the organization ID as the parent. This command is specifically designed to retrieve immediate child folders of an organization, not nested folders.

Answer

gcloud organizations list

Answer

gcloud projects list --filter='parent.id:123456789012'

Answer

gcloud resource-manager folders list --folder=123456789012