Google Cloud DevOps Engineer Sample Questions With Full Explanations
Here are 8-10 multiple-choice questions designed to match the difficulty and style of the Google Cloud DevOps Engineer exam. They cover key domains including CI/CD, monitoring, logging, SRE practices, and infrastructure as code.
Instructions: Choose the single best answer for each question. These questions focus on practical application and troubleshooting within Google Cloud, mirroring the scenario-based approach of the actual certification. Readers also benefit from Google Professional Data Engineer Sample Questions With Full Explanations.
Question 1
You are implementing a CI/CD pipeline for a microservices application deployed on Google Kubernetes Engine (GKE). You need to ensure that a build failure in one service does not block the deployment of other independent services. Which Cloud Build configuration approach best satisfies this requirement?
A. Configure a single Cloud Build trigger that builds and deploys all services sequentially.
B. Use a single Cloud Build trigger with a build config that uses a Dockerfile per service, but fail the entire build if any single service fails.
C. Create separate Cloud Build triggers for each microservice, each with its own build config and deploy step.
D. Use a multi-module Cloud Build config in a single repository, and only deploy the changed module.
Answer: C
Explanation: Separate triggers for each service allow independent build and deployment lifecycles. If one fails, the others continue. Option A and B create tight coupling and a single point of failure; Option D still centralizes the pipeline logic, making it harder to isolate failures.
Question 2
Your SRE team is investigating a sudden spike in latency for a service. You need to determine if the spike correlates with a recent code deployment or an external factor like a traffic surge. Which Google Cloud tool should you use to simultaneously analyze logs, metrics, and trace data?
A. Cloud Monitoring alone
B. Cloud Logging alone
C. Cloud Trace alone
D. Cloud Monitoring and Cloud Logging combined with Cloud Trace
Answer: D
Explanation: A holistic view requires correlating metrics (latency), logs (errors), and traces (request paths). Cloud Monitoring provides metrics, Cloud Logging provides logs, and Cloud Trace provides distributed tracing. Using them together (e.g., via Metrics Explorer or dashboards) is the correct approach. The other options only provide partial information.
Question 3
You are configuring a Dataflow pipeline for real-time stream processing. You need to ensure that the pipeline can automatically restart from the last successful checkpoint in case of a zone failure. What is the most reliable method to achieve this?
A. Use Cloud Scheduler to restart the Dataflow job every hour.
B. Enable the "Update" option on the Dataflow job and manually re-run it after a failure.
C. Use the Dataflow service's built-in automatic restart and checkpointing features, ensuring the pipeline is not manually stopped.
D. Run a cron job on Compute Engine to monitor and restart the pipeline using the gcloud dataflow jobs run command.
Answer: C
Explanation: Dataflow provides native fault tolerance with checkpointing and automatic worker restart. It uses persistent disk snapshots to recover from zone failures without manual intervention. Options A, B, and D are manual or scheduled workarounds that do not leverage the built-in, more reliable recovery mechanism.
Question 4
Your organization requires that all infrastructure changes be peer-reviewed before being applied. You are using Terraform to manage GKE clusters. Which approach enforces this requirement while maintaining a single source of truth?
A. Developers apply Terraform changes directly to production after local testing.
B. Use a Git repository as the source of truth and a CI/CD pipeline (e.g., Cloud Build) that runs terraform plan and requires a manual approval step before terraform apply. Our post on AWS Data Engineer Associate Sample Questions With Full Explanations covers this further.
C. Store Terraform state in a local file and share it via email for review.
D. Use Cloud Deployment Manager instead of Terraform to avoid the need for review.
Answer: B
Explanation: Option B implements a GitOps workflow where changes are proposed via pull requests, reviewed, and then a pipeline executes the plan. The pipeline can be configured to pause for approval before applying. Options A and C lack a formal review process. Option D doesn't inherently solve the review requirement.
Question 5
You are designing a logging strategy for a high-throughput application. You need to exclude verbose debug logs from production to reduce costs, but you must retain audit logs for compliance. Which approach is the most efficient and cost-effective?
A. Send all logs to Cloud Logging and use log-based metrics to filter out debug logs.
B. Configure the logging agent to filter logs based on severity before they are sent to Cloud Logging.
C. Store all logs in Cloud Storage and use a separate process to delete debug logs.
D. Disable all logging in production to save costs.
Answer: B
Explanation: Filtering at the source (the logging agent or application logger) reduces the volume of data ingested, which is the primary cost driver in Cloud Logging. Option A still ingests the logs (costing money) even if they are not shown by default. Option C is inefficient, and Option D violates compliance requirements.
Question 6
You have a multi-region GKE deployment. You need to roll out a new version of a service to 10% of users in one region, analyze the error rate, and then proceed to 100% if stable. Which strategy best fits this requirement?
A. Blue/Green deployment with a single global load balancer.
B. Canary deployment using a regional load balancer and a weighted backend service.
C. Rolling update with a maxSurge of 10%.
D. Recreate deployment strategy by deleting the old version first.
Answer: B
Explanation: A canary deployment allows you to route a specific percentage of traffic (10%) to the new version. Using a regional load balancer allows you to target one specific region. Blue/Green (A) doesn't easily support percentage-based routing to a single region. Rolling update (C) gradually replaces instances but doesn't give precise traffic control. Recreate (D) causes downtime.
Question 7
A user reports a 503 error when accessing a service. You look at Cloud Monitoring and see high CPU utilization on the instance group. You need to diagnose if the issue is a bottleneck in the application code or a lack of capacity. What is the first step you should take?
A. Immediately increase the number of instances in the managed instance group.
B. Restart the instances to clear the high CPU.
C. Analyze the latency distribution (e.g., p50, p99) and request error rates in Cloud Monitoring to see if the problem is systemic or isolated to specific requests.
D. Roll back the last deployment to the previous version.
Answer: C We recommend pairing this with AWS Machine Learning Engineer Sample Questions With Full Explanations.
Explanation: Before scaling or rolling back, you must understand the cause. Analyzing latency and error rates helps determine if the high CPU is due to a code bug (e.g., infinite loop causing high CPU for all requests) or a genuine capacity issue (e.g., all requests are slow due to high load). Scaling (A) might be a temporary fix but doesn't solve the root cause. Restarting (B) and rolling back (D) are disruptive and should be done only after analysis.
Question 8
You are automating the creation of a GKE cluster. You need to ensure that the kubectl configuration is automatically available to subsequent steps in your CI/CD pipeline without manual intervention. How should you achieve this?
A. Instruct developers to run gcloud container clusters get-credentials manually after the pipeline runs.
B. Use the gcloud container clusters create command and then use gcloud container clusters get-credentials in the same pipeline step to generate the kubeconfig file.
C. Create a static kubeconfig file and commit it to the source repository.
D. Use the GKE API to create the cluster and then manually download the kubeconfig from the console.
Answer: B
Explanation: The get-credentials command is a standard CLI command that fetches the cluster's authentication info and updates the kubeconfig file. Running it as a step in the pipeline makes the cluster available to subsequent steps. Option A is manual. Option C is insecure and static. Option D is not automated.
Question 9
Your SLO is 99.9% availability over a 30-day window. You are using Cloud Monitoring to track this. The service experiences an outage. What is the most reliable way to determine if you have violated your SLO?
A. Calculate the total downtime manually from the incident report.
B. Use the Cloud Monitoring SLO monitoring feature to view the SLO's error budget and burn rate.
C. Check if the error rate is above 0.1% for any single minute.
D. Review the number of 500 errors in the Cloud Logging console.
Answer: B
Explanation: Cloud Monitoring's SLO feature is specifically designed to track error budgets and burn rates against your defined SLO. It provides a precise, automated calculation over the rolling 30-day window. Manual calculation (A) is error-prone. Option C is incorrect because SLOs are measured over a period, not a single minute. Option D only counts 500 errors, not time-based availability.
Question 10
You are building a multi-project environment (e.g., prod, dev, test). You need to enforce that a specific VPC network configuration is applied to the prod project, but you want to manage it centrally. Which tool and approach is best for this?
A. Use Cloud Deployment Manager templates and apply them manually to each project.
B. Use Terraform with a module for the VPC, and use a separate state file for the prod project with a CI/CD pipeline that requires higher approval permissions.
C. Create the VPC manually in the prod project and use gcloud commands to replicate it to other projects.
D. Use a single Terraform state file for all projects to keep them in sync.
Answer: B
Explanation: Terraform modules promote reusability and consistency. Using separate state files for different environments (e.g., production) is a best practice to isolate changes and reduce the risk of a blast radius. The CI/CD pipeline can enforce stricter approval processes for the prod state. Option A is manual and error-prone. Option C is not automated. Option D is a bad practice as it couples all environments and increases risk.
These questions should give you a solid feel for the scenario-based, multi-service nature of the exam. Remember to focus on the why behind each answer, not just the correct letter, as the real exam will often present you with multiple "good-looking" answers.
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