This module builds practical infrastructure literacy for data engineers working with AI-native systems. Learners examine compute, serving, vector databases, orchestration, monitoring, and environment setup, then apply those concepts to document platform assumptions, governance boundaries, and readiness for later repository and CI/CD work.

This module teaches learners how to organize AI-native data engineering work in a reproducible repository and support it with foundational Git and CI/CD practices. Learners build a clean project structure, apply branching and review concepts, identify repository hygiene risks, and draft a minimal workflow skeleton with jobs, artifacts, logs, and approvals.

This module teaches learners to use AI to draft SQL, helper code, unit tests, and data tests, then apply structured human review to inspect, refactor, validate, and document those artifacts before acceptance. Learners build a reviewed artifact set and evidence bundle that demonstrate safe, accountable AI-assisted engineering practice.

This module teaches learners how to design and validate AI-augmented data workflows using DAG and orchestration concepts, dependency mapping, schedule reasoning, and operational documentation. Learners use AI to draft workflow logic, then apply human review to validate dependencies, document failure assumptions, and define safe automation boundaries for production-ready pipeline automation.

This module teaches learners to create and validate the metadata and documentation artifacts that make AI-native data products understandable, reusable, and governable. Learners practice producing schema summaries, dataset cards, lineage descriptions, and validation evidence while using AI assistance responsibly and checking documentation claims against actual metadata, tests, and workflow context.

This module brings together prior course artifacts into a governed CI/CD workflow that can validate, document, and approve AI-augmented data engineering changes. Learners implement meaningful gates, connect policy and review evidence to responsible AI practices, and assemble a final project package that is traceable, reviewable, and ready for submission.

This closing module helps learners celebrate completing Course 2, reflect on its major themes, and connect their progress to the next step in the certificate. It reinforces the professional mindset of using automation and AI assistance responsibly while previewing Course 3 at a high level without introducing new technical instruction.

The Final Exam assesses your ability to apply the course’s engineering principles across AI systems, automation, CI/CD, and data workflows. You will evaluate design choices, governance controls, and operational practices to identify the most defensible, reproducible, and reviewable solutions.