CCA-F Preparation Curriculum — Module 0 of 8

New here? Complete the Dev Environment Setup Guide before starting Week P1.

Prerequisite Track: Python Fundamentals & Infrastructure Basics (Weeks P1–P5)

Complete before Module 1 | ~33–40 hours total


Why this prerequisite track exists

The main CCA-F curriculum (Modules 1–8) produces an architect who can design, specify, govern, deploy, and operate enterprise AI systems. The prerequisite track exists to close the gap between design intent and execution reality: an architect who cannot write a Python script, containerise a service, or navigate a cloud console cannot verify that their designs work, cannot diagnose production failures, and cannot be credible to engineering teams implementing their specifications.

This track does not make you a software engineer. Five weeks of focused work produces an architect who can read and write Python at a professional standard, understands how cloud infrastructure is organised, can containerise a service, and can push code through a basic deployment pipeline. That is the minimum viable technical floor for the production-readiness outcomes this curriculum targets.

Estimated time commitment: 6–8 hours per week for 5 weeks. Each week has pre-work (readings + setup), a core concept section, a step-by-step exercise producing a tangible artefact, and a self-check. All exercises produce runnable code that you will extend in later modules.

How the exercises work — three tiers of independence. Module 0 uses three levels of exercise so you always know what to attempt on your own and when it is legitimate to look something up.

The 15-minute rule: if you are genuinely stuck on any exercise after 15 minutes of real effort, use the reference for that week:

Looking at the reference is not failure. Abandoning the exercise is.

Tools you will need — install before Week P1:


How this track supports the CCA-F exam

Module 0 does not directly address any exam domain. Its purpose is to build the technical floor that every exam domain rests on. The table below shows which prerequisite capabilities feed which exam domains in Modules 1–8.

Prerequisite Capability Acquired Underpins Exam Domain
Python functions, data structures, error handling Weeks P1–P2 All 5 domains — code fluency is assumed across every hands-on exercise
HTTP API calls, JSON parsing, environment variable loading Week P3 Domain 1 (agentic loops), Domain 2 (tool integration), Domain 4 (structured output)
Docker containerisation and image management Week P4 Domain 1 (production deployment), Domain 5 (reliability architecture)
Git branching, pull requests, and repository management Week P4 All domains — portfolio artefacts are delivered via GitHub
AWS Lambda, Secrets Manager, GitHub Actions CI/CD Week P5 Domain 1 (operational context), Domain 5 (context management across deployed services)

A learner who reaches Module 1 without genuine fluency in any row of this table will encounter that gap at the worst possible moment — inside a timed module exercise with no time to step back and learn the foundational skill. The progression gate at the end of this module is the checkpoint. Treat it as a performance standard, not a reading list.


Weeks

P1

Python Fundamentals I: Language and Data Structures

Write Python functions that manipulate strings, lists, and dictionaries, handle errors gracefully, and produce correctly formatted output — without reference to documentation.

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P2

Python Intermediate: The Patterns You Will Use Every Day

Write and confidently read list comprehensions, use lambda functions in one-line contexts, apply @property and basic inheritance in class design, validate function inputs using isinstance() and descriptive ValueError raises, use re.match() and re.search() to detect patterns in text, and recognise what a decorator does when you encounter @app.get or @task — without needing to write a custom decorator from scratch.

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P3

Python Fundamentals II: Files, APIs, and Environment Management

Write Python scripts that load configuration from environment variables, call an HTTP API, parse the JSON response, and write structured output to a file — without hardcoding any credentials or URLs.

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P4

Infrastructure Basics: Containers, Cloud, and Git

Build a Docker image for a Python AI service, push it to a container registry, explain the three-tier cloud infrastructure model, and manage your codebase through a branching Git workflow.

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P5

Hands-On Deployment: Cloud Functions, Secrets, and Basic CI/CD

Deploy your containerised Claude service to a cloud function or managed container service, configure secrets management using the cloud provider's native service, verify the deployment via curl, and run a basic automated test on each commit using GitHub Actions.

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