The Problem
Learners in the 30 Days of Google Cloud program often stall on Qwiklabs challenge labs because the UI gives limited guidance and the official docs assume prior experience. Without a concise, step‑by‑step reference, participants waste time troubleshooting basic configuration errors.
What This Does
The repository is a curated collection of markdown walkthroughs that capture the essential commands, UI actions, and screenshots for twelve Qwiklabs labs across the Cloud Engineering and Data Science & ML tracks. Each lab is represented by a single file such as Deploy to Kubernetes in Google Cloud: Challenge Lab.md that lists the required console steps, CLI snippets, and visual cues (e.g., screenshots/cluster.png). The assets/ folder supplies branding graphics used in the README, while LICENSE and README.md provide the legal and high‑level context.
No executable code, scripts, or infrastructure definitions are included; the repo’s value lies in its documentation artefacts.
How To Use It
Clone or download the repository. No build tools are required. git clone https://github.com/DSC-IIIT-Kalyani/qwiklabschallenges.git cd qwiklabschallenges Select a lab from the list in README.md or open the markdown file directly, e.g., cat "Deploy to Kubernetes in Google Cloud: Challenge Lab.md". Follow the ordered steps inside the file. CLI commands are embedded as fenced blocks; screenshots in screenshots/ illustrate expected UI states. Validate completion by cross‑checking the final screenshot reference (e.g., screenshots/job3.png) with your own Qwiklabs console output.
No environment variables, secret files, or external dependencies are referenced in the repository.
Real‑World Use
A training coordinator can embed the markdown files into a learning management system (LMS) as static resources. For example, a simple script could generate HTML pages for each lab:
import markdown, pathlib
for md in pathlib.Path('.').glob('*.md'): html = markdown.markdown(md.readtext()) (pathlib.Path('html') / f'{md.stem}.html').write_text(html)
Learners then browse the HTML version while performing the lab in Qwiklabs, reducing context switching.
Code Health & Issues
Med – No test coverage – The repo contains only documentation; there are no unit or integration tests to verify the accuracy of the listed commands. Med – No CI/CD pipeline – Absence of .github/ workflows or other CI configuration means changes are not automatically linted or validated. Low – Potential drift – Qwiklabs UI and APIs evolve; without a maintenance process, markdown steps may become outdated. Low – License present – LICENSE file exists, but the README does not explicitly reference the license type, which could cause reuse ambiguity.
No obvious security concerns (e.g., embedded credentials) are present.
The Bottom Line
The repository delivers a practical, searchable set of lab guides that can accelerate onboarding for Google Cloud training participants. Its utility is limited to documentation; the lack of automated testing or CI means the material requires periodic manual verification to stay current. Ideal for small training teams or individual learners who need a quick reference, but not suited as a foundation for automated learning pipelines.