GitLab CI/CD Specialist Interview Questions

The GitLab CI/CD Specialist certification is proof that you’re a DevOps whiz. It’s an accredited degree that helps you tackle any automated process with confidence. A GitLab CI/CD Specialist is also eligible to apply for advanced degrees in continuous integration and receive preferential admission. To come out of the interview room with flying colors, you need to show that you can tackle any automated process with confidence. Having decent skills and knowledge of applying DevOps best practices using GitLab will certainly help get the job of your dream. You may take our GitLab CI/CD Specialist online tutorial along with our free practice tests to brush up your knowledge to pass the exam and the interview.

Want to become a GitLab CI/CD Specialist by proving your proficiency in DevOps processes and best practices? Here’s a list of some of the top GitLab CI/CD Specialist interview questions that will help you pass the interview in one go!

Advanced Interview Questions

How do you set up and configured GitLab CI/CD pipeline in the past?

setting up and configuring a GitLab CI/CD pipeline typically involves the following steps:

1. Install and set up GitLab: Install and set up GitLab on a server or in the cloud, and create a new project to host the codebase.
2. Create a .gitlab-ci.yml file: Create a .gitlab-ci.yml file in the root of the codebase, this file will be used to define the pipeline stages and jobs.
3. Define pipeline stages: Define pipeline stages, such as build, test, and deploy.
4. Define pipeline jobs: Define pipeline jobs, such as compiling the code, running tests, and deploying to different environments.
5. Add environment variables: Add environment variables, such as API keys, to the pipeline so they can be used in the pipeline jobs.
6. Configure runners: Configure runners, which are the agents that will run the pipeline jobs.
7. Test the pipeline: Test the pipeline by committing code changes and observing the pipeline stages and jobs running.
8. Monitor and optimize: Monitor the pipeline and optimize the pipeline performance by analyzing the pipeline logs and metrics.
9. Secure the pipeline: Secure the pipeline by using GitLab built-in security features and following best practices for securing the pipeline.
10. Integrate with other tools and platforms: Integrate with other tools and platforms, such as slack, email, or external systems for notifications, deployments or other integration.

It’s important to keep in mind that the specific steps and configuration will depend on the requirements of the organization and the specific pipeline that needs to be set up.

What are some of challenges faced while managing complex CI/CD pipeline?

An example of a complex pipeline that could be managed by a GitLab CI/CD specialist can be a pipeline for a microservices-based application that runs on a Kubernetes cluster. The pipeline includes several stages, such as:

• Building the microservices using a build tool, such as Maven or Gradle.
• Running unit tests and static code analysis using tools like JUnit and SonarQube.
• Pushing the built images to a container registry, such as Docker Hub or GitLab Container Registry.
• Deploying the microservices to a development environment on the Kubernetes cluster using Helm charts.
• Running integration tests on the development environment using tools like Postman or Newman.
• Deploying the microservices to a staging environment on the Kubernetes cluster using Helm charts.
• Running acceptance tests on the staging environment using tools like Selenium or Appium.
• Approving the pipeline to deploy the microservices to a production environment on the Kubernetes cluster using Helm charts.

The challenges that the pipeline manager might face in this scenario could include:

• Managing the complexity of deploying to a Kubernetes cluster and ensuring that the pipeline is able to handle different environments and configurations.
• Ensuring that the pipeline is secure and compliance with the organization’s policies and regulatory requirements.
• Managing the scaling and load of the pipeline, especially when the application is handling large traffic.
• Troubleshooting and resolving issues with the pipeline, especially when the pipeline is dealing with different types of technologies and tools.
• Managing the pipeline performance and monitoring the pipeline’s health.
• Managing the pipeline integration with other tools and platforms, for example integrating with monitoring and logging tools.

It’s important to keep in mind that the specific pipeline and challenges will depend on the requirements of the organization and the specific application that is being developed and deployed.

What are some of the important things to keep in mind when handling security and compliance in a GitLab CI/CD pipeline?

Handling security and compliance in a GitLab CI/CD pipeline is an important aspect of managing the pipeline. Here are some steps to consider when handling security and compliance in a GitLab CI/CD pipeline:

1. Use built-in security features: GitLab provides built-in security features such as Multi-Factor Authentication (MFA), Access Control, and Audit Logs that can be configured to meet your organization’s security requirements.
2. Use GitLab Container Registry: Use GitLab Container Registry to store and manage your Docker images, this will provide security features such as image signing and scanning for vulnerabilities.
3. Use GitLab Secrets: Use GitLab Secrets to store and manage sensitive information, such as API keys or credentials, in a secure way.
4. Follow best practices for securing the pipeline: Follow best practices for securing the pipeline, such as using secure protocols, encrypting sensitive data, and limiting access to the pipeline.
5. Use GitLab’s built-in compliance features: GitLab provides built-in compliance features, such as SOC2, ISO27001, HIPAA, and GDPR that can be configured to meet your organization’s compliance requirements.
6. Use third-party tools and services: Use third-party tools and services, such as security scanners, to scan for vulnerabilities and ensure that your pipeline is secure.
7. Regularly review and update your pipeline: Regularly review and update your pipeline to ensure that it is secure and compliant with the organization’s policies and regulatory requirements.
8. Provide training: Provide training to your team members on how to handle security and compliance in the pipeline and how to use the security features provided by GitLab.
9. Monitor the pipeline: Monitor the pipeline to detect any security breaches or compliance violations and take appropriate actions.
10. Continuously improve: Continuously improve the pipeline’s security and compliance by using the latest features and best practices.

It’s important to keep in mind that the specific steps and best practices will depend on the requirements of the organization and the specific pipeline that needs to be secured and compliant.

How do you handle scaling and managing large-scale deployment of GitLab CI/CD?

Scaling and managing large-scale deployment of GitLab CI/CD can be done in several ways.

1. One approach is to use GitLab’s built-in features for scaling, such as using multiple runners, setting up a shared runner pool, and configuring autoscaling for runners. Additionally, you can use Kubernetes to run GitLab and its associated services, which can help with scaling and managing the deployment.
2. Another approach is to use GitLab’s API to automate and manage the deployment process. This can be done using a tool like Ansible, Terraform, or Helm to manage the deployment of GitLab and its associated services.
3. Additionally, monitoring and logging are key to managing large-scale deployment of GitLab CI/CD. GitLab provides built-in monitoring and logging features, such as Prometheus and Grafana for monitoring and Logging for logging.
4. Furthermore, it is important to keep the GitLab instance up to date with the latest security patches and updates, as well as regularly backing up the instance to ensure that data can be recovered in case of a failure.
5. Finally, it is important to test and validate the deployment process and infrastructure before deploying to production to ensure that it can handle the load and scale as needed.

How do you troubleshoot and resolve issues with a GitLab CI/CD pipeline?

Troubleshooting and resolving issues with a GitLab CI/CD pipeline can be done in several ways:

1. Review pipeline logs: The first step in troubleshooting a pipeline issue is to review the pipeline logs. GitLab provides detailed logs for each pipeline run, which can be accessed from the pipeline’s page in the GitLab UI. These logs can provide information about the pipeline’s execution, including any error messages that were generated.
2. Check pipeline configuration: Another step is to check the pipeline configuration. Verify that the pipeline is configured correctly and that all necessary environment variables and settings are in place. Make sure that the correct branches and paths are specified in the pipeline configuration.
3. Check environment: Verify that the environment in which the pipeline is running is configured correctly. This includes checking that all required dependencies are installed and that the correct versions are being used.
4. Check permissions: Verify that the user or service account executing the pipeline has the correct permissions to access the necessary resources.
5. Check for external dependencies: Check if there are any external dependencies that are causing the pipeline to fail, such as a service being down or a network issue.
6. Check for known issues: Check if the issue has been reported and if there is a workaround or a fix available. GitLab maintains a public issue tracker where known issues and their status are tracked.
7. If none of the above steps work, you can reach out to the GitLab support team for further assistance, providing as much information as possible about the problem, such as error messages, pipeline logs, and the pipeline configuration.

Can you explain how you use GitLab Runner and how it fits into the CI/CD pipeline?

GitLab Runner is a tool that is used to run jobs and send the results back to GitLab. It is an open-source tool that is designed to work with GitLab and is integrated into the GitLab CI/CD pipeline. When a pipeline is triggered, the Runner picks up the job, runs the script defined in the .gitlab-ci.yml file and sends the results back to GitLab. The runner can run the job on the same machine where GitLab is installed, or it can run the job on a different machine. This allows the pipeline to be run on a variety of environments, such as different operating systems or different cloud providers.

Once the pipeline is completed, the runner sends the results back to GitLab, which then updates the pipeline status and provides detailed logs of the pipeline execution. GitLab Runner supports multiple types of executors such as shell, Docker, SSH, Kubernetes and more. That means you can use the same runner for multiple types of jobs, for example, running a shell script on the same machine as GitLab, and running a Docker container on a different machine.

Additionally, GitLab Runner can be installed and run in a variety of ways, such as using the official GitLab Runner Docker image, installing it as a binary, or using it as a Helm chart on Kubernetes.

In summary, GitLab Runner is an essential component of the GitLab CI/CD pipeline, and it allows you to run jobs and test your code automatically as part of the pipeline, and it supports a wide variety of use cases and environments.

How do you manage and monitor the performance of a GitLab CI/CD pipeline?

Managing and monitoring the performance of a GitLab CI/CD pipeline can be done in several ways:

1. Pipeline metrics: GitLab provides built-in metrics for pipelines, such as pipeline duration, build time, and the number of successful and failed pipelines. These metrics can be viewed in the GitLab UI, and they can also be exported to external monitoring tools such as Prometheus or Grafana for further analysis.
2. Logging: Logging is an important aspect of monitoring the performance of a pipeline. GitLab provides built-in logging features, such as Logging, that can be used to view the logs of pipeline runs. The logs can provide information about the pipeline’s execution, including any error messages that were generated.
3. Error tracking: Error tracking is another important aspect of monitoring the performance of a pipeline. GitLab provides built-in error tracking features, such as GitLab Error Tracking, that can be used to track and analyze errors that occur in a pipeline.
4. Performance testing: Performance testing can be used to identify bottlenecks and potential issues with a pipeline. Performance testing can be done using tools such as Apache JMeter, Gatling or LoadRunner, and it can be integrated into the pipeline using GitLab CI/CD
5. Alerts: Setting up alerts can help to notify the team when there are issues with the pipeline. GitLab provides built-in alerting features, such as Prometheus Alerts, that can be used to notify the team when a pipeline fails or when the pipeline performance degrades.
6. Audit logs: GitLab provides audit logs that enable tracking of user actions in GitLab, this can be useful to monitor and identify any anomalies or suspicious activities that could impact pipeline performance.
7. Continuous monitoring: Continuously monitoring the pipeline performance can help to identify issues and potential bottlenecks early on, and it can also help to identify patterns and trends over time.

By using the above methodologies, you can effectively manage and monitor the performance of a GitLab CI/CD pipeline, and quickly identify and resolve any issues that may arise.

How do you handle integration of GitLab CI/CD with other tools and platforms?

Handling integration of GitLab CI/CD with other tools and platforms can be done in several ways:

1. GitLab API: GitLab provides a comprehensive API that can be used to integrate GitLab CI/CD with other tools and platforms. The API can be used to automate and manage the pipeline process, as well as retrieve information about pipeline runs and pipeline status.
2. Webhooks: GitLab provides webhooks that can be used to trigger actions in other tools and platforms when certain events occur in GitLab. For example, when a pipeline completes, a webhook can be used to trigger a deployment to a specific environment or notify a chat tool.
3. Third-party integrations: GitLab has a wide range of built-in integrations with other tools and platforms, such as Kubernetes, Slack, Jira, and more. These integrations can be configured directly from the GitLab UI, which can simplify the integration process.
4. GitLab CI/CD Variables: GitLab CI/CD allows you to define variables that can be used in the pipeline, these variables can be used to store sensitive information such as passwords, tokens, and keys that can be used to authenticate against other tools and platforms.
5. Scripting: GitLab CI/CD provides the ability to run scripts in the pipeline, these scripts can be used to interact with other tools and platforms, for example, you can use a script to deploy to a cloud platform or to perform a database migration.
6. GitLab CI/CD Runners: GitLab Runners supports multiple types of executors such as shell, Docker, SSH, Kubernetes and more. That means you can use the same runner for multiple types of jobs, for example, running a shell script on the same machine as GitLab, and running a Docker container on a different machine.

By using the above methods, you can effectively integrate GitLab CI/CD with other tools and platforms, automate the pipeline process, and streamline the integration process.

How do you stay up-to-date with the latest features and updates for GitLab CI/CD?

There are a few ways to stay up-to-date with the latest features and updates for GitLab CI/CD:

1. Subscribe to the GitLab blog and newsletter to receive notifications of new updates and features.
2. Follow GitLab on social media platforms such as Twitter and LinkedIn to stay informed about the latest news and updates.
3. Join the GitLab community on GitLab.com and participate in discussions about new features and updates.
4. Review the release notes for each new version of GitLab, which provide detailed information about new features and updates.
5. Attend GitLab webinars and events, where the company often announces new features and updates.
6. Use GitLab’s built-in release page where it will provide the release information and the features added to the release.

Basic Interview Questions

1. What is CI/CD in GitHub?

The software development process called continuous integration (CI) involves the automated building, testing, and integration of code changes within a shared repository. Similarly, continuous delivery or deployment involves automatically delivering code changes to production-ready environments or directly deploying code changes to customers.

2. How does GitLab CI work?

Continuous integration (CI) begins with your GitLab instance. You can share new code via merge requests, and you can initiate a pipeline of automated processes. The build, test, and deploy pipelines will run after code is committed to the repository. Continuous delivery (CD) allows you to take your project from concept to completion. It does this by putting your features into production after automated pipelines have been validated through CI.

3. Is GitHub the same as GitLab?

Because both GitHub and GitLab are version control systems, it can be difficult to choose one of the two. The most significant difference between the two is that GitHub primarily focuses on collaboration and code review, whereas GitLab focuses on DevOps as well as continuous integration and continuous delivery (CI/CD).

4. Could you tell me the requirements for CI-CD?

Continuous integration (CI) requires developers to merge their changes to the main code branch many times per day. Each code merges trigger an automated build and test sequence so that developers can quickly detect problems early on in the software development lifecycle.

5. What are CI/CD pipeline stages?

There are four stages of a CI/CD pipeline: 

• Source Stage
• Build Stage
• Test Stage
• Deploy Stage

6. Could you explain how does a CI CD pipeline works?

A continuous integration/continuous delivery pipeline automates the software delivery process. The pipeline builds code, runs tests, and safely deploys a new version of the application. Automated pipelines remove manual errors, provide standardized feedback loops to developers, and enable fast product iterations.

7. How do you manage a CI/CD pipeline?

The main principles of CI are that you:

1. Check-in code infrequently.
2. Automate the build and test portion.
3. Always test the code locally before checking it in.
4. Never merge any failed branches to the main branch.
5. Return its status back to successful if you’re the developer who causes the failed build or test

8. What is the function of the CI system?

The objective of CI is to establish a dependable system for building, packaging, and testing software. This consistency provides an efficient environment for frequent code changes and collaboration, which leads to better software quality.

9. Could you explain the working of the GitLab CD?

GitLab CI/CD integrates seamlessly into an existing development workflow. You can set up a project by discussing a feature implementation in an issue and working locally on the proposed change, then pushing commits to a feature branch in a remote repository that’s hosted in GitLab. When you push your commits to the feature branch, the CI/CD pipeline for the project is triggered.

10. How would you set environment variables in GitLab CI?

When using variables there is no need to hard code values. In GitLab, CI/CD variables can be defined by going to Settings » CI/CD » Variables, or by simply defining them in the.gitlab-ci.yml file

11. How do you use variables in the GitLab pipeline?

GitLab variables can be used to pre-define the values. Go to your project page, Settings tab -> CI/CD, find Variables and click on the Expand button. Here you can define variable names and values, which will be automatically passed into the GitLab pipelines, and are available as environment variables there.

12. What is deployment in testing?

The deployment of new applications and modules, updates, and patches from developers to users depends on the methods used by developers to build, test, and deploy code. The speed with which a product responds to customer preferences or requirements depends on these methods. Deployment methods can also impact the quality of code changes.

13. Could you explain the process of deployment?

The deployment process flow consists of 5 steps: 

• Planning
• Development
• Testing
• Deploying
• Monitoring

14. What is the difference between build and deploy?

Build and deploy are testing terms with important meanings in the IT field. “Build” means consolidating and combining a set of executable code for testing. “Deploy” means injecting that set of executable code into a particular software environment to test it.

15. How would you define the GitLab workflow?

The Flow process incorporates a pre-prod branch to fix bugs before merging changes back to main (typically from dev) before going to production. Each team can have as many pre-prod branches as needed — for example, from main to test, from test to acceptance, and from acceptance to production.

16. Can you elaborate on the release flow?

Flow is a trunk-based development approach in which the master branch is deployed and developed. Branches are created off the master branch when there are special needs, but the branches do not serve as deployment targets. Here a pull request merges a branch back into the master branch.

17. What is the most important deployment step?

Systematic communication is an important element of deployment management. Without communication, many problems can arise, including scheduling conflicts and misunderstandings about the scope of a deployment. Post-deployment check-ins with decision-makers can help your team arrive at timely and accurate decisions, allowing you to avoid many common deployment issues.

18. What are artifacts in GitLab CI?

In GitLab 12.4, files and directories from internal and private projects can be previewed when access control is enabled using GitLab Pages. Jobs can output an archive of all the files and directories that are generated as part of the job run in a .zip file. This output is known as a job artifact, which can be downloaded using the GitLab UI or API.

19. Could you explain what are deployment artifacts?

A deployment artifact, also known as an “archive file,” is the file generated by a build that contains all the information required to deploy the application to runtime. This type of file is created in the design phase and then handed off to the run-time environment.

20. Where are artifacts stored in GitLab CI?

The artifacts are stored in /home/git/gitlab/shared/artifacts by default. In order to keep them backed up regularly, you should save the file and restart GitLab.

21. What is cache dependency?

Cache dependencies enable the system to expire cached items when related objects are modified automatically. The system uses dummy cache keys, or cache items with no data that represent one or more other objects, to create dependencies between cached data and other objects.

22. What do you know about the dependency proxy?

GitLab provides a local cache called the Dependency Proxy for frequently-accessed upstream images. The Dependency Proxy can act as a pull-through cache in the case of CI/CD by receiving a request from your build server and returning the requested image from the registry.

23. How would you explain Docker layer caching?

 Docker Layer Caching (DLC) is a useful feature to enable during continuous integration and continuous delivery (CI/CD) jobs in order to create reusable images that do not change each time they are run. Docker Layer Caching (DLC) is a feature that helps reduce the time it takes to run your containers by keeping track of changes to images so that they do not have to be rebuilt every time you upgrade your container.

24. How does GitLab caching work?

A cache is a set of files that a job can download before the execution, and upload after completion. The default location of the cache is the home directory of the GitLab Runner. If the distributed cache is configured, S3 works as storage. After execution, it will upload the cache back to the S3 bucket (push).

25. What is a GitLab registry?

GitLab Container Registry is a secure and private registry for Docker images. Built on open-source software and tightly integrated with GitLab, Container Registry gives every user—from solo developers to large enterprise—a single, secure interface to manage their projects, as well as all their containerized applications and services.