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Learn Claude Certifications From GitHub

The repository and the website are equal learning surfaces. The website adds interactive figures and browser progress. GitHub gives your AI coding harness the lesson source, scenario code, tests, artifacts, quizzes, diagnostics, and track order it needs to teach you step by step.

Start With an AI Tutor

Clone the repository so the tutor can run every lab and test:

git clone https://github.com/rohitg00/ai-engineering-from-scratch.git
cd ai-engineering-from-scratch

Claude Code discovers the repository tutor automatically. Start with:

/claude-certification

For Codex, Cursor, or another local agent that reads SKILL.md, install the portable course skills:

npx skills add rohitg00/ai-engineering-from-scratch

Then invoke /claude-certification. For ChatGPT or any harness that does not install local skills or support slash commands, attach or open this repository and paste this prompt:

Read skills/claude-certification/SKILL.md completely. Use it to choose my
Claude certification track, create my learning plan, and teach me one lesson
at a time with the real labs, artifacts, quizzes, and remediation in this repo.

The tutor asks about your goal, experience, pace, and whether you want the track diagnostic. It writes CLAUDE-CERTIFICATION.md, then resumes from that file in later sessions. Each lesson requires you to:

  1. explain the decision in your own words;
  2. predict and manipulate the lesson scenario;
  3. run the checked-in lab and tests;
  4. build or defend your own artifact;
  5. pass the lesson quiz;
  6. remediate weak exam domains before advancing.

Your work belongs under learning-artifacts/claude/, separate from the completed reference artifacts in each lesson.

Choose a Route

Track Best fit Route Diagnostic Full mock
CCAO-F Knowledge work, analysis, validation, and responsible Claude use 9-lesson route 16 questions 60 questions
CCDV-F Engineers building and securing Claude applications 15-lesson route 16 questions 53 questions
CCAR-F Builders defending Claude Code, Agent SDK, API, MCP, and orchestration choices 21-lesson route 15 questions 60 questions
CCAR-P Senior engineers and architects owning discovery through operations 25-lesson route 14 questions 63 questions

The track JSON is the machine-readable source for route order, prerequisite coverage, domain weights, study plans, and assessment paths. The tutor reads it instead of guessing from a generic study plan.

Use Guided No-Code Mode for Associate

CCAO-F does not require software-development experience. Its lessons still ship Python because a deterministic validator makes the policy, evidence, workflow, and review rubrics testable. The tutor can run that code for you; you are not required to write it.

Paste this after installing or opening the tutor:

Start me on CCAO-F in guided no-code mode. Run the local validators for me,
teach every scenario interactively, and help me create each learner-owned
workflow, policy, evidence, or review artifact from my decisions. Do not skip
the practical work or quizzes, and do not require me to write Python.

You will still predict outcomes, manipulate scenarios, defend choices, revise failed artifacts, and take the original assessments. The interface changes; the evidence standard does not.

Learn One Lesson Manually

Every certification lesson has the same GitHub contract:

certifications/claude/lessons/NN-lesson/
├── docs/en.md          full lesson and interactive-lab reasoning
├── code/main.py        scenario runner, simulator, scorer, or validator
├── code/tests/         deterministic verification
├── outputs/            completed reference artifact
└── quiz.json           six grounded questions with explanations

Open the next lesson path from your selected track. Read docs/en.md, predict the scenario result, then run:

LESSON=certifications/claude/lessons/27-enterprise-governance-compliance-and-hitl
python3 "$LESSON/code/main.py"
python3 -m unittest discover -s "$LESSON/code/tests" -v

Lesson 27 is a governance example: its runnable work validates a policy and human-review packet. It does not add artificial provider code to a conceptual topic. Other lessons ship threat models, ADRs, approval flows, evidence bundles, tool-loop simulators, RAG reports, API lifecycle labs, and capstone verifiers.

Use outputs/ as the completed example. Create your own version in learning-artifacts/claude/<exam-code>/<lesson-slug>/, run the validator against a copy when supported, and record the evidence in CLAUDE-CERTIFICATION.md.

Run the Whole Local Verification Suite

From the repository root:

python3 scripts/audit_certifications.py

find certifications/claude/lessons -path '*/code/main.py' -print0 \
  | xargs -0 -n1 env -u ANTHROPIC_API_KEY -u ANTHROPIC_MODEL python3

find certifications/claude/lessons -path '*/code/tests/test_*.py' -print0 \
  | xargs -0 -n1 env -u ANTHROPIC_API_KEY -u ANTHROPIC_MODEL python3

The lesson 30 live Messages API test skips unless credentials are explicitly provided. The default curriculum is local and credential-free. For the optional wire check, use environment variables only and follow that lesson's instructions. Never put an API key in source, a prompt, or a learning-state file.

Take Assessments From GitHub

Each track declares one diagnostic and one original full mock. An AI tutor can read the JSON and administer it one question at a time:

  • answer single questions with one letter;
  • answer multiple questions with the full set of letters;
  • use exact-set scoring with no partial credit;
  • keep answers and explanations hidden until submission;
  • report raw percentage and per-domain results;
  • follow internal lesson references for every miss.

Practice percentages are course scores. They are not Anthropic scaled scores, credentials, or guarantees of passing.

Use the Website Too

The same curriculum remains available at aiengineeringfromscratch.com/certifications.html. Use it for direct-manipulation figures, local browser progress, timers, and visual assessment remediation. GitHub remains the better surface when you want an AI tutor to run code, inspect artifacts, and preserve a detailed learning plan.

For a local website preview:

node site/build.js
python3 -m http.server 4173 --bind 127.0.0.1

Open http://127.0.0.1:4173/site/certifications.html.

Independence and Publishing Boundary

This is independent community preparation. It is not affiliated with, endorsed by, sponsored by, or authorized by Anthropic. It uses public objectives and original scenarios, does not contain live exam questions, and does not issue a credential or guarantee a passing result. Check the current official guide and eligibility rules before registering.

Certification content is published through GitHub and the website. It is intentionally not included in the repository's EPUB/PDF book workflow because the labs, assessments, route state, and interactive mechanisms are the course.