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GitLab CI/CDInterview Q&A

Most asked interview questions with detailed answers

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Last updated Jul 2026
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GitLab CI Interview Q&A

Q: GitLab CI vs GitHub Actions?

GitLab CI: native to GitLab, powerful include/extends, better multi-project pipelines, built-in registry and K8s integration. GitHub Actions: better marketplace, OIDC support, free for public repos. GitLab preferred for self-hosted enterprise.

Q: What are GitLab CI stages?

Stages define execution order. Jobs within same stage run in parallel. Default stages: build, test, deploy. Custom: stages: [lint, build, test, security, deploy].

Q: What is extends in GitLab CI?

Reuse job configurations. Define a .template job, extend it in real jobs. Reduces duplication. Hidden jobs (prefix with .) don't run but can be extended.

Q: How do GitLab CI rules differ from only/except?

rules is the modern replacement for only/except. Rules are evaluated top-to-bottom; first matching rule applies. More powerful: combine conditions (if, changes, exists, when). only/except is simpler but cannot express complex conditions. GitLab recommends using rules for all new pipelines.

Q: What is a GitLab CI/CD component vs a template?

CI/CD components (GitLab 16.0+): versioned, reusable pipeline configuration distributed via the GitLab CI/CD Catalog. Input parameters defined, versioned with semantic versioning. Templates: YAML includes from GitLab's template library — less structured. Components are the future of pipeline reuse in GitLab.

Q: How does GitLab handle secrets?

Variables defined in Settings > CI/CD > Variables. Masked variables (hidden in logs), protected variables (only on protected branches/tags). Integration with HashiCorp Vault: secrets: keyword fetches from Vault dynamically at job start. External secrets manager is preferred over GitLab variables for sensitive credentials.

Q: What are GitLab runners and how do you scale them?

Runners execute CI jobs. GitLab.com provides shared runners. Self-managed: install gitlab-runner on Linux/Docker/Kubernetes. Docker executor: each job gets fresh container (most common). Kubernetes executor: each job is a Kubernetes pod — auto-scales with cluster. Group/project runners for dedicated performance.

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