GitLab Certified Git Associate (GLCFA)

When Git habits vary across a team, the result is merge conflicts, inconsistent branching, unreviewed changes, and pipelines that only one person understands. This course gives IT Directors and Team Leads a shared baseline. It runs from Git fundamentals and branching strategy through GitLab team workflows and CI/CD automation, and it prepares learners for the GitLab Certified Git Associate exam.

Role fit: This beginner-level course suits junior DevOps admins and IT Practitioners with about a year of Git experience. Experienced engineers can skip the early Git skills and move straight to the GitLab and CI/CD material.

Time investment: The course has 19 skills totaling about 17 hours, with each skill running 50–60 minutes. That works out to roughly 4–6 weeks at 3–4 hours per week per learner.

Risk and governance: The course covers several areas that reduce risk:

• Granting least-privilege IAM permissions for AWS pipeline accounts
• Delegating pipeline credentials through HashiCorp Vault
• Standardizing GitLab CI/CD workflows so teams can review, run, and troubleshoot pipelines consistently

Planning notes:

• Several demos assume a self-hosted GitLab instance.
• The Vault integration requires GitLab access that supports HashiCorp Vault integration. Plan sandbox access before assigning the course.

How to roll it out: Learners follow along hands-on in the terminal and the GitLab Web UI. Team Leads can preview the first skill, then assign the course as a structured path so the whole team adopts the same branching and pipeline conventions.

IT teams complete this training to move from ad hoc Git usage to a consistent, automated GitLab workflow that the whole organization can rely on. The scenarios come straight from day-to-day DevOps work: parallel feature development across branches, code review inside GitLab projects, and CI/CD pipelines that build containers and deploy cloud infrastructure.

• Fewer broken merges and lost work: Practitioners learn branching strategies, merge versus rebase, and how to stage and unstage changes. This helps them switch between multiple features in parallel without corrupting history.
• A standardized team workflow in GitLab: Team Leads can organize projects and repositories into groups, set permissions, and use GitLab's project features so developers collaborate in one place and do not bypass review by habit.
• Repeatable, auditable deployments: Your team builds pipelines that package Docker images and deploy to AWS, Microsoft Azure, Ansible-managed hosts, and Terraform environments. Separate dev and production state and a deployment history support change tracking.
• Safer credential handling and faster troubleshooting: Practitioners learn to scope IAM permissions for automation accounts and to integrate HashiCorp Vault so pipelines can be granted access to credentials (a Premium or Ultimate GitLab tier is required). They also learn to monitor GitLab Runner and pipeline performance at the task level, so slow builds can be diagnosed instead of guessed at.

Knowledge

• How Git tracks current and historical repository state inside the hidden .git directory, and why that matters when troubleshooting
• When to use branches, how a branching strategy is structured, and how the HEAD pointer behaves in attached and detached states
• The difference between merge and rebase operations, and how each affects branch history
• How GitLab projects, groups, and permissions fit into a team development workflow, and how GitLab compares with other Git hosting options
• How GitLab CI/CD pipelines are defined in the .gitlab-ci.yml file, the role of GitLab Runner infrastructure, and why granular pipeline metrics speed up performance troubleshooting
• How artifacts, service containers, and HashiCorp Vault integration support more secure, more capable pipelines

Ability

• Initialize repositories, stage and unstage files, and manage commits across parallel feature branches with the Git CLI
• Work with GitLab projects in the web UI and use Git workflows to share repository changes with a team
• Build Docker container images inside GitLab CI/CD pipelines so application code and build files live in one repository
• Deploy cloud resources to AWS and Microsoft Azure from pipelines, using scoped IAM credentials and the right policy permissions
• Run Terraform projects from GitLab, including separate Terraform state for dev and production environments
• Delegate pipeline credential access through HashiCorp Vault instead of hard-coding secrets