What you'll learn
⚡ Understand and apply agents and workflows concepts including agent architecture and agent construction with claude
⚡ Evaluate agent patterns and frameworks in the context of agents and workflows
⚡ Understand and apply applications and integration concepts including understanding requirements and systems life cycle
⚡ Evaluate claude api mechanics in the context of applications and integration
⚡ Understand and apply claude code concepts including claude code operation
⚡ Understand and apply eval, testing, and debugging concepts including debugging and error handling
⚡ Understand and apply model selection and optimization concepts including llm fundamentals and technical fundamentals
⚡ Evaluate model selection and tradeoffs in the context of model selection and optimization
⚡ Understand and apply prompt and context engineering concepts including context engineering and prompt engineering
⚡ Evaluate output handling in the context of prompt and context engineering
Requirements
❗ No specific prerequisites required. Some familiarity with CCDV-F-related concepts is helpful but not mandatory, this course teaches everything from the ground up.
Description
This course contains the use of artificial intelligence. However, every lecture recording involves me reading the scripts, and I am fully involved in scripting and production. Be careful buying courses with instructors that don't appear in person. AI courses are becoming quite common on learning platforms.
This course is a complete, structured study program for the Anthropic Claude Certified Developer - Foundations (CCDV-F) exam. Built domain by domain against the official exam blueprint, it covers every topic area you need to understand before sitting for the exam. Each lesson is a narrated video that explains how concepts connect to each other and to real-world practice, not just what the definition is, but how a practitioner applies it.
D1:Agents and Workflows (14% of the exam), covering workflow vs. agent: decision criteria and tradeoffs, manager/supervisor hierarchies and the role of subagents, claude agent sdk: query(), the agent loop, and input modes, custom agent loops and harnesses, managed agent deployment: self-hosted vs. anthropic-hosted, hooks for deterministic actions, agent design patterns: tool-use loops, subagents, memory, context-window management, agentic frameworks: langgraph vs. strands vs. pydanticai (identities and tradeoffs). You will understand how each of these areas is tested on the exam and how they connect to real-world practice.
D2:Applications and Integration (33% of the exam), covering deriving functional requirements from business needs, infrastructure requirements and solution-architecture fit, systems life cycle: develop, implement, operate, maintain, messages api structure and data-access patterns, tools, streaming, and vision, extended/adaptive thinking and prompt caching, message batches api: realtime vs. batch tradeoffs, invoking claude via third-party vendors (bedrock, vertex), rest apis and json, asynchronous programming patterns, version control and sdlc integration, code review and refactoring, instruction interpretation across interfaces (claude code / desktop / claudeai / api / sdks), content boundaries and schema design, session hygiene, plugin management, claudemd files and hierarchy, settingsjson configuration, model version pinning and prompt versioning, plugin dependencies. You will understand how each of these areas is tested on the exam and how they connect to real-world practice.
D3:Claude Code (3% of the exam), covering core components: rules, skills, commands, agents, agent memory, session management and slash commands (built-in + custom), headless / streaming / auto modes, claudemd hierarchy, repo init, settingsjson. You will understand how each of these areas is tested on the exam and how they connect to real-world practice.
D4:Eval, Testing, and Debugging (2% of the exam), covering error type identification and recovery strategy selection, trace analysis and problem-origin isolation (integration layer vs. model output). You will understand how each of these areas is tested on the exam and how they connect to real-world practice.
D5:Model Selection and Optimization (16% of the exam), covering llm basics: tokens, context windows, sampling, non-determinism, next-token generation, model options: fast mode, extended/adaptive thinking, effort levels, fundamental prompting: zero-, single-, multi-shot, sdks that wrap rest apis, websockets and connection fundamentals, opus vs. sonnet vs. haiku: use cases and quality/latency/cost tradeoffs, breaking behavior changes across model releases, token usage tracking and cost modeling, caching for cost: prompt caching and cache check-pointing. You will understand how each of these areas is tested on the exam and how they connect to real-world practice.
D6:Prompt and Context Engineering (11% of the exam), covering context window management and drift/bloat prevention (tool-output pruning, compaction), context isolation via subagents and multi-step workflows, instruction clarity, system vs. user placement, output constraints, few-shot examples and iterative refinement, input sanitization, structured output patterns and response validation, defensive parsing and skepticism toward confident output. You will understand how each of these areas is tested on the exam and how they connect to real-world practice.
D7:Security and Safety (8% of the exam), covering prompt injection and jailbreak defense, untrusted input, data-leakage prevention, and pii handling, content policy and guardrail layering, secure-by-design: privacy, iam, least privilege, hooks as guardrails: preventing destructive actions, managing secrets, credentials, and api keys (dev + prod), identity validation, access approval, and authorized-access monitoring. You will understand how each of these areas is tested on the exam and how they connect to real-world practice.
D8:Tools and MCPs (10% of the exam), covering tool use and function calling: descriptions and schemas, tool error handling and usage patterns (agentic dispatch, client vs. server, approval), tool set construction best practices, mcp server authoring: resources, tools, prompts, mcp communication patterns: stdio, sockets, client vs. server, choosing among built-in tools, custom tools, skills, and mcps (tradeoffs). You will understand how each of these areas is tested on the exam and how they connect to real-world practice.
Every domain includes practice questions designed to mirror the style and difficulty of CCDV-F exam scenarios, covering not just recall but application and analysis. The course closes with full-length practice exams with detailed answer explanations, so you can measure your readiness and focus your remaining study time where it matters most.
Major topics covered: workflow vs. agent: decision criteria and tradeoffs, manager/supervisor hierarchies and the role of subagents, claude agent sdk: query(), the agent loop, and input modes, custom agent loops and harnesses, managed agent deployment: self-hosted vs. anthropic-hosted, hooks for deterministic actions, agent design patterns: tool-use loops, subagents, memory, context-window management, agentic frameworks: langgraph vs. strands vs. pydanticai (identities and tradeoffs), deriving functional requirements from business needs, infrastructure requirements and solution-architecture fit, systems life cycle: develop, implement, operate, maintain, messages api structure and data-access patterns, tools, streaming, and vision, extended/adaptive thinking and prompt caching, message batches api: realtime vs. batch tradeoffs, invoking claude via third-party vendors (bedrock, vertex), rest apis and json, asynchronous programming patterns, version control and sdlc integration, code review and refactoring, instruction interpretation across interfaces (claude code / desktop / claudeai / api / sdks), content boundaries and schema design, session hygiene, plugin management, claudemd files and hierarchy, settingsjson configuration, model version pinning and prompt versioning, plugin dependencies, core components: rules, skills, commands, agents, agent memory, session management and slash commands (built-in + custom), CCDV-F exam prep 2026.
Who this course is for
⭐ Software developers and engineers who build applications with the Claude API and want to validate their foundational knowledge with realistic practice exams.
⭐ AI and backend engineers integrating Claude into products who want blueprint-weighted practice across agents, tools, prompting, and the SDK before sitting the CCDV-F exam.
⭐ Developers new to the Claude platform who want to test their grasp of the developer workflow against exam-style questions with detailed explanations.
⭐ Technical practitioners moving from experimentation to production who want to confirm they are ready for the developer foundations credential.
⭐ Anyone preparing for the Anthropic CCDV-F exam who wants two full-length practice tests with detailed explanations across all eight domains.



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