Google Professional Cloud DevOps Engineer Interview Questions

Interview preparation is just as crucial as test preparation. As a result, preparing for an interview necessitates far more practice and confidence than studying for any other exam. You must make the finest first impression possible. So, in order to aid our applicants in their interview preparation, we have done our best to provide you with the greatest and expert-revised interview questions. Furthermore, we have covered all levels of questions from basic to intermediate to advanced. As a result, we strongly advise applicants to prepare with the finest and accomplish the best. But first, let’s take an overview of the Google Professional Cloud DevOps Engineer.

Advanced Interview Questions

What experience do you have with cloud technologies, specifically with Google Cloud Platform (GCP)?

As a Google Professional Cloud DevOps Engineer, I have extensive experience in designing, implementing, and maintaining cloud-based infrastructure using Google Cloud Platform. I have been using GCP for over 4 years, and I have gained in-depth knowledge and expertise in different GCP services such as Compute Engine, Kubernetes Engine, App Engine, Cloud Storage, Cloud SQL, Cloud Functions, Stackdriver, and BigQuery, among others.

I have also worked on large-scale cloud migrations from on-premise to GCP and have helped organizations achieve high levels of availability, scalability, and security. My experience also includes setting up CI/CD pipelines, monitoring, and disaster recovery solutions. I have a good understanding of cloud computing principles and how to optimize cost and performance.

Overall, my experience with Google Cloud Platform has allowed me to deliver secure, scalable, and cost-effective solutions for various clients, helping them achieve their cloud computing goals.

What is your experience with Continuous Integration and Continuous Deployment (CI/CD)?

Continuous Integration (CI) is a software development practice where developers integrate code into a shared repository frequently, typically several times a day. The goal of CI is to identify and fix integration issues as soon as possible, thereby reducing the risk of potential bugs and errors.

Continuous Deployment (CD) takes the practice of CI one step further by automatically deploying code changes into production as soon as they are tested and validated. This helps reduce the time and manual effort required to deploy code changes, improving the speed and reliability of software updates.

I have experience with various CI/CD tools, including Jenkins, TravisCI, CircleCI, and Google Cloud Build. I have also implemented CI/CD pipelines for various applications, including web applications, mobile applications, and microservices. I am proficient in integrating various tools, such as source control systems (e.g. Git), build tools (e.g. Maven, Gradle), and deployment tools (e.g. Ansible, Terraform) into CI/CD pipelines.

How do you handle infrastructure as code in GCP?

As a Google Professional Cloud DevOps Engineer, I handle infrastructure as code (IAC) in GCP through the use of various tools and technologies.

1. Terraform: This is an open-source tool that allows us to create, manage, and manipulate infrastructure as code. It helps us in automating the deployment and management of resources on GCP.
2. Cloud Deployment Manager: This is a GCP service that enables us to define, deploy, and manage cloud resources using YAML templates. The templates can be version-controlled, making it easier to track changes and revert back if needed.
3. Google Cloud SDK: The Google Cloud SDK provides command-line tools and libraries for managing resources on GCP. We can use the SDK to automate the creation of resources and manage them through scripts.
4. Kubernetes: Kubernetes is a powerful container orchestration tool that we can use to manage containers and infrastructure as code. With Kubernetes, we can define, deploy, and manage our resources in a scalable and efficient manner.

These are some of the ways we handle infrastructure as code in GCP. By using these tools, we can ensure that our infrastructure is consistent, repeatable, and scalable, making it easier for us to manage and maintain our systems.

Can you explain how you would set up a scalable and highly available infrastructure in GCP?

As a Google Professional Cloud DevOps Engineer, I would approach setting up a scalable and highly available infrastructure in GCP in the following way:

1. Identifying the requirements: The first step would be to identify the requirements for the infrastructure such as the number of users, data storage requirements, and performance expectations. This information would help me determine the appropriate size and configuration of the resources required.
2. Designing the architecture: Based on the requirements, I would design a scalable and highly available architecture that incorporates load balancing, auto-scaling, and multiple availability zones. This would ensure that the infrastructure can handle changes in demand and that there are no single points of failure.
3. Implementing the infrastructure: I would use infrastructure as code (IAC) to implement the architecture. This would include setting up virtual machines, load balancers, firewalls, and storage. I would also use automation tools like Terraform or Cloud Deployment Manager to automate the deployment and management of resources.
4. Configuring monitoring and logging: I would set up monitoring and logging for the infrastructure using tools like Stackdriver, Prometheus, and Grafana. This would help me detect and respond to any issues in real-time.
5. Establishing disaster recovery: I would establish a disaster recovery plan that includes regular backups, snapshots, and the ability to recover from a disaster in a different location. This would help ensure that the infrastructure remains highly available even in the event of a disaster.
6. Testing and refining the infrastructure: Finally, I would perform regular testing and refining of the infrastructure to ensure that it remains scalable and highly available. This would include load testing, performance testing, and regular security audits.

What experience do you have with Kubernetes and container orchestration?

I have worked on various projects where I have implemented and maintained production-ready Kubernetes clusters. I have experience with deploying, scaling, and managing containerized applications on Kubernetes clusters.

I am familiar with the Kubernetes architecture and its various components, including nodes, pods, services, and volumes. I have also worked on creating and managing Kubernetes manifests, including deployment, service, and config maps.

I have experience with using Kubernetes tools such as Helm, kubectl, and kubeadm for deploying and managing applications. I am also familiar with using Kubernetes dashboards, such as Grafana and Prometheus, for monitoring and managing the health of the cluster.

In addition, I have experience with integrating Kubernetes with other cloud services, such as Google Cloud Platform and Amazon Web Services, to create scalable and highly available containerized applications.

Overall, my experience with Kubernetes and container orchestration has allowed me to effectively design, deploy, and manage large-scale applications in a production environment.

What is your experience with automating infrastructure and application deployment?

As a Google Cloud DevOps Engineer, automating infrastructure and application deployment is a key aspect of my work. I have experience using tools like Terraform, Ansible, or Cloud Deployment Manager to automate the provisioning, configuration, and management of infrastructure and applications in the cloud. This includes defining infrastructure as code, setting up automated pipelines for continuous integration and delivery, and automating deployment and scaling processes. I also have experience working with containerization technologies such as Docker and Kubernetes, which are widely used for automating application deployment. With these tools, I am able to ensure consistent and efficient deployment of infrastructure and applications, reduce manual errors and improve overall speed and reliability of the deployment process.

Can you explain how you would approach monitoring and logging in a GCP environment?

I would approach monitoring and logging in a GCP environment by following these steps:

1. Define the requirements: Before starting the monitoring and logging process, I would gather information about the system’s architecture, critical services, and data flow. This will help me understand the various components that need to be monitored and logged.
2. Select the appropriate tools: GCP offers various tools for monitoring and logging, such as Stackdriver, Cloud Logging, and Cloud Monitoring. I would select the right tools based on the requirements defined in step 1.
3. Configure the monitoring and logging tools: After selecting the appropriate tools, I would configure them to collect data from the various components and services of the system. This may include setting up alerts, custom dashboards, and log retention policies.
4. Integrate with existing systems: If the organization already has existing monitoring and logging systems, I would integrate the GCP tools with them to ensure seamless data flow and centralized management.
5. Implement logging for critical services: I would implement logging for critical services, such as application logs, database logs, and network logs, to capture relevant information for debugging and auditing purposes.
6. Monitor and review logs regularly: I would set up regular monitoring and review of logs to identify any issues or patterns that may indicate potential problems. This will help to proactively identify and resolve issues before they become major problems.
7. Update and refine the monitoring and logging process: As the system evolves, I would continuously refine and update the monitoring and logging process to ensure that it remains effective and relevant.

By following these steps, I would be able to effectively monitor and log a GCP environment, ensuring that critical services are functioning as expected, and potential issues are identified and addressed proactively.

Have you worked with security and compliance in a cloud environment?

As a Google Professional Cloud DevOps Engineer, security and compliance is an essential aspect of cloud infrastructure management. In a cloud environment, data security and regulatory compliance are critical concerns that must be addressed. To ensure security and compliance, a number of best practices must be followed, such as:

1. Encryption of sensitive data: Encryption of sensitive data, both at rest and in transit, is crucial to ensuring data security in the cloud. Google Cloud provides various encryption options to encrypt data stored in Google Cloud Storage, BigQuery, and other storage services.
2. Identity and access management: Implementing a robust identity and access management (IAM) system is critical to controlling access to sensitive data and resources. Google Cloud IAM provides granular access controls that can be used to define who has access to what resources and at what level.
3. Network security: Implementing network security controls, such as firewalls, virtual private networks (VPNs), and access control lists (ACLs), is crucial to protecting sensitive data and resources from unauthorized access. Google Cloud provides various network security options to secure your resources.
4. Compliance with regulations: Compliance with regulations, such as GDPR, HIPAA, PCI-DSS, and others, is critical to ensuring that data is handled in accordance with applicable laws and regulations. Google Cloud provides various compliance certifications and reports, such as SOC 2, ISO 27001, and PCI-DSS, to help you meet regulatory requirements.

In conclusion, security and compliance is an ongoing process in a cloud environment, and it requires continuous monitoring and improvement. As a Google Professional Cloud DevOps Engineer, it’s important to stay up-to-date with best practices and tools available to ensure the security and compliance of cloud infrastructure.

Can you discuss a time when you troubleshot and resolved a complex issue in a production environment?

Yes, as a Google Professional Cloud DevOps Engineer, I have experience troubleshooting and resolving complex issues in production environments.

One specific example I recall involved a production service that was experiencing high latency and intermittent failures. After conducting a thorough investigation, I discovered that the root cause was a network bottleneck due to the service’s high traffic volume.

To resolve the issue, I worked with the networking team to implement a new load balancing strategy that distributed traffic more effectively across multiple servers. Additionally, I optimized the service’s database queries to reduce the load on the database, which improved its overall performance.

After implementing these changes, the service’s latency was reduced and its reliability improved, resolving the issue and restoring normal operation. Through this experience, I demonstrated my ability to quickly identify and resolve complex production issues, and my commitment to delivering high-quality, stable services.

What is your experience with GCP networking, including VPNs, subnets, firewall rules, and load balancing?

As a Google Professional Cloud DevOps Engineer, I have extensive experience in GCP networking and have been working with various components of it, including VPNs, subnets, firewall rules, and load balancing.

VPNs: I have worked with both site-to-site VPNs and client-to-site VPNs on GCP, leveraging the cloud VPN service provided by Google. I have experience in configuring the VPN gateways and setting up routing between the on-premise and cloud networks.

Subnets: I have hands-on experience in creating subnets and configuring routing tables to manage the flow of network traffic within a VPC. I have also worked on designing and implementing custom VPCs, including creating custom subnets and private IPs for different resources.

Firewall rules: I have experience in setting up firewall rules in GCP to manage network traffic and secure the resources within a VPC. I have worked on configuring firewall rules to allow and deny traffic based on source IP, port, and protocol, and have also implemented custom firewall rules to meet specific business requirements.

Load balancing: I have worked on deploying various types of load balancers on GCP, including network load balancers, HTTP(S) load balancers, and SSL proxy load balancers. I have experience in configuring load balancing to distribute incoming traffic across multiple instances and ensuring high availability and scalability of applications.

In conclusion, my experience with GCP networking has provided me with the knowledge and expertise to design, deploy, and manage secure and scalable network solutions on the cloud.

Basic Interview Questions

About the exam:

The primary goal of the Google Professional Cloud DevOps Engineer (GCP) Exam is to assess the quality and competency of professionals in using cloud platform strategies. Candidates are assessed on their capacity to manage responsibilities in the efficient growth of operations, as well as their ability to balance service dependability and delivery speed. Candidates should have a strong understanding of how to use Google Cloud Platform to construct software delivery pipelines, deploy and monitor services, and manage and learn from problems.

Now, let’s begin with the Google Professional Cloud DevOps Engineer Interview Questions.

What is Cloud Computing?

Cloud computing is the on-demand, pay-as-you-go distribution of IT services through the Internet. Instead of purchasing, operating, and maintaining physical data centres and servers, you may rent computing power, storage, and databases from a cloud provider like Google on an as-needed basis. The cloud computing service is genuinely worldwide, with no regional or border limits.

What is Google Cloud Platform?

Google Cloud Platform is a Google-developed cloud platform that allows users to access cloud systems and computer services. GCP provides a wide range of cloud computing services in the compute, database, storage, migration, and networking sectors. Google Cloud Platform (GCP) is a set of cloud computing services that run on the same infrastructure as Google’s end-user products, such as Google Search, Gmail, file storage, and YouTube.

What types of tools are available via the Google Cloud Platform?

• Firstly, Compute.
• Secondly, Networking.
• Thirdly, Storage and Databases.
• Fourthly, Artificial Intelligence (AI) / Machine Learning (ML)
• Fifthly, Big Data.
• Sixthly, Identity and Security.
• Lastly, Management Tools

What does a Professional Cloud DevOps Engineer do?

• A Professional Cloud DevOps Engineer is in charge of ensuring that development processes are efficient and that service dependability and delivery pace are balanced.
• They know how to develop software delivery pipelines, install and monitor services, and manage and learn from incidents using the Google Cloud Platform.

Give the full form of SLO?

The SLO stands for Service-Level Objective.

What do you mean by Service-Level Objective (SLO)?

When a system is unavailable, it is unable to execute its purpose and will fail by default. Any future discussions regarding whether the system is working sufficiently and dependably, as well as what design or architectural modifications should be made to it, must be framed in terms of this system’s ability to satisfy the SLO.

Give the full form of SLA?

The SLA stands for Service-Level Agreement.

What do you understand by Service-Level Agreement (SLA)?

An SLA usually entails a promise to someone who uses your service that its availability SLO will meet a specific level over a set period of time, and that if it does not, a penalty will be paid.

Give the full form of SLI?

The SLI stands for Service-Level Indicator.

Explain Service-Level Indicator (SLI)?

We use the SLI to calculate the service availability percentage when determining if our system has been functioning within SLO for the preceding week. You must be able to quantify the rates of successful and failed inquiries as your SLIs if a user wants to know how dependable your service is.

What do you understand by Toil?

Rather of being strategy-driven and proactive, toil is interrupt-driven and reactive. It takes a lot of effort to manage pager notifications. We may never be able to totally eliminate this sort of job, but we must constantly strive to reduce it.

How to implement a learning culture?

• Firstly, Create a training budget, and advocate for it internally.
• Secondly, Ensure that your team has the resources to engage in informal learning and the space to explore ideas.
• Thirdly, Make it safe to fail.
• Fourthly, Create opportunities and spaces to share information.
• Lastly, Make resources available for continued education.

What does managing images?

Listing images in a repository, adding tags, deleting tags, copying photos to a new repository, and deleting photographs are all examples of image management.

What is the use of Universal build artifact management system?

Your business can manage container images and language packages in one place with Artifact Registry. It comes with support for native artefact protocols and is completely integrated with Google Cloud’s tooling and runtimes. This makes it simple to set up automated pipelines by integrating it with your CI/CD tooling.

Define Heterogeneous deployments?

Depending on the deployment’s specifications, heterogeneous deployments are referred to as “hybrid,” “multi-cloud,” or “public-private.” Heterogeneous deployments cover various regions in a single cloud environment, numerous public cloud environments, or a mix of on-premises and public cloud settings.

What are Multi-cloud deployments?

Some of the most typical heterogeneous deployment patterns utilized with Kubernetes include multi-cloud deployments, in which all deployments are very identical. One typical use of multi-cloud deployments is to set up a highly available deployment that can withstand the breakdown of any one environment. The same Kubernetes deployment may be orchestrated in any of the appropriate settings.

What are the key features of Cloud Build?

• Firstly, Extremely fast builds
• Secondly, Automate your deployments
• Thirdly, Support for multi-cloud
• Fourthly, Commit to deployment in minutes
• Lastly, Unparalleled privacy

Give some objectives of continuous delivery pipeline using Google Kubernetes Engine (GKE)?

• Set up your environment by launching Cloud Shell and deploying Spinnaker for Google Cloud.
• Create a GKE cluster to deploy the sample application to.
• Create a Git repository for a sample app and upload it to Cloud Source Repositories.
• Build your Docker image.
• Create triggers to create Docker images when your app changes.
• Create a Spinnaker pipeline to deploy your app to GKE reliably and consistently.Deploy a code change, triggering the pipeline, and watch the change deploy to production.

Name different types of Cloud Audit Logs?

• Admin Activity audit logs
• Data Access audit logs
• System Event audit logs
• Policy Denied audit logs

How version control helps meet continuous delivery?

The version control helps you meet these critical requirements:

• Reproducibility. Teams must be able to provision any environment in a fully automated fashion and know that any new environment reproduced from the same configuration is identical. A prerequisite for achieving this goal is having the scripts and configuration information that are required to provide an environment stored in a shared, accessible system.
• Traceability. Teams should be able to pick any environment and determine the versions of every dependency used to generate that environment fast and precisely. They should also be able to compare and contrast two copies of an environment to see what has changed.

What is multi-tenancy?

Multiple users and workloads, referred to as “tenants,” share a multi-tenant cluster. To limit the harm that a compromised tenant may inflict to the cluster and other tenants, multi-tenant cluster operators must segregate tenants from one another. In addition, cluster resources must be distributed evenly across tenants.

What are the advantages of using multi-tenants?

• Firstly, Reduced management overhead
• Secondly, Reduced resource fragmentation
• Lastly, No need to wait for cluster creation for new tenants

What is a Logging agent?

A Logging agent sends logs to Logging from a variety of third-party applications and system components. Additional logs can be streamed by configuring the agent.

What is an Ops agent?

Compared to the basic Cloud Logging agent, the Ops agent integrates logging and metrics into a single agent and is targeted for specific logging workloads that require faster throughput and/or improved resource efficiency. If you have a lot of work to do and the agent’s provided feature set suits your demands, you should go for the Ops Agent.

What is alerting?

Problems in your cloud apps are alerted in real time, allowing you to immediately rectify them. An alerting policy in Cloud Monitoring specifies the conditions under which you want to be notified and how you want to be notified. This page gives you a quick rundown of alerting policies.

What are the key features of Cloud Logging?

• Logs explorer
• Custom logs / Ingestion API
• Alerting on logs
• Error Reporting
• Lastly, Cloud Audit Logs

What are Cloud audit logs?

Cloud Audit Logs helps security teams maintain audit trails in Google Cloud. Moreover, with this tool, enterprises can attain the same level of transparency over administrative activities and accesses to data in Google Cloud as in on-premises environments.

What is the use of Cloud Monitoring?

• Firstly, Collect metrics from multi-cloud and hybrid infrastructure in real-time
• Secondly, Enable SRE best practices extensively used by Google based on SLOs and SLIs
• Thirdly, Visualize insights via dashboards and charts, and generate alerts
• Fourthly, Collaborate by integrating with Slack, PagerDuty, and other incident management tools
• Lastly, Day zero integration for Google Cloud metrics

Explain the Cloud Debugger feature?

Cloud Debugger is a unique feature of Google Cloud Platform that allows you to analyse an application’s status at any code point without halting or slowing it down. Cloud Debugger makes it easy to see the current status of an application without having to add logging lines.

Define Cloud Profiler?

Cloud Profiler is a low-cost, statistical profiler that continuously collects CPU and memory use data from your production apps. It assigns that data to the application’s source code, allowing you to see which areas of the app are consuming the most resources and revealing the code’s performance characteristics.