Tool or Technology
TT0170

Amazon Q Developer Foundations

Code, Debug & Build Smarter on AWS with AI

Amazon Q Developer
Duration
8 Hours
Level
Basic
Delivery
Instructor-Led
Format
Workshop

Programme Objectives

  • Build foundational proficiency in using Amazon Q Developer for AI-assisted software development and AWS-related development activities.
  • Apply structured prompts and codebase context to generate, explain, modify, optimise, and refactor code.
  • Use agentic coding capabilities to perform multi-step development tasks while retaining developer oversight.
  • Apply Amazon Q Developer for debugging, testing, code review, security analysis, and application modernisation.
  • Build responsible AI-assisted development workflows incorporating validation, permissions, security, and human review.

Technology covered

Amazon Q DeveloperIDE ChatInline Code SuggestionsAgentic CodingWorkspace ContextCode ReviewsSecurity ScanningCode TransformationsModel Context Protocol (MCP)AWS Development Guidance

Who should attend

  • Software Developers
  • AWS Cloud Developers
  • Frontend Developers
  • Backend Developers
  • Full-Stack Developers
  • Application Developers
  • Software Engineers
  • DevOps Engineers
  • QA & Test Automation Engineers
  • Cloud Engineers
  • Technical Leads & Solution Developers

Prerequisites & Participant Readiness

  • Basic programming knowledge in at least one programming language
  • Familiarity with functions, classes, files, dependencies, and common software-development concepts
  • Basic experience using an IDE or code editor
  • Basic familiarity with Git and source-code repositories is helpful
  • Foundational AWS knowledge is beneficial but not mandatory
  • No previous Amazon Q Developer experience required

TOC Modules

Concepts
  • Understanding Generative AI and coding assistants in the software-development lifecycle
  • Understanding Amazon Q Developer and its role in software and AWS development
  • Understanding IDE Chat, inline suggestions, workspace context, and agentic coding
  • Understanding AI assistance versus developer-controlled implementation
  • Understanding strengths, limitations, and human accountability for AI-generated code
Practical activities
  • Exploring Amazon Q Developer in a supported development environment
  • Asking Amazon Q to explain a simple application component
  • Completing a guided code modification
  • Reviewing generated changes before acceptance

Scenarios

AWS Application Requirement to Tested Feature

Business Requirement → Codebase Analysis → Amazon Q Development Assistance → Code Generation → Testing → Security Review → Developer Approval

Participants use Amazon Q Developer to understand an existing application, implement a defined feature, generate tests, identify potential quality or security issues, and prepare validated changes for integration.

Legacy Application to Modernised Codebase

Existing Application → Modernisation Assessment → Transformation Plan → AI-Assisted Code Transformation → Build & Tests → Diff Review → Approved Modernised Application

Participants use Amazon Q Developer's transformation workflow to understand how legacy application code can be modernised while validating generated changes, compatibility, tests, and business functionality.

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