CCA-F Claude Certified Architect - Complete Exam Preparation

Learn why the Claude API is a practical foundation for building real AI systems and preparing for the CCA-F exam.

Meet the course project, ShopAssist AI, which you will use to apply Claude architecture concepts in a realistic workflow.

Understand how an LLM processes input, generates responses, and behaves inside an AI application.

Create your first Claude API request and learn the basic structure of sending input and receiving output.

Learn how multi-turn conversations work and why managing conversation history is essential for reliable AI systems.

Build a simple chat loop and use helper patterns to manage repeated interaction with Claude.

Understand how system prompts shape model behavior and how they differ from backend logic and business rules.

Learn how model choice, temperature, and stop sequences affect response quality, control, and reliability.

Learn how prompt engineering can turn vague AI behavior into more predictable and useful results.

Review the Domain 4 objectives and understand what the exam expects around prompt engineering and evaluation.

Learn how to build an eval loop that tests prompt performance and guides systematic improvement.

Explore different ways to grade AI outputs using code-based checks, model-assisted review, and human judgment.

Learn how clear instructions and XML-style structure can improve prompt clarity, consistency, and output control.

Understand the basic idea of retrieval-augmented generation and why retrieval can improve answer relevance before generation.

Learn how explicit criteria and few-shot examples help Claude understand quality expectations more reliably.

Refine prompts by adjusting criteria, examples, and detail level to improve precision and reduce weak outputs.

Learn why structured output matters and how Claude can be guided to return consistent, machine-readable results.

Understand how tool schemas and tool_choice help control Claude’s responses and enable structured tool use.

Learn how tool use can make structured output more reliable than relying on plain text formatting alone.

Review the specific exam-relevant differences and details related to structured output.

Learn how validation, retry logic, confidence signals, and human review improve reliability in production systems.

Understand which output problems can be fixed automatically and which require escalation or redesign.

Learn how to process multiple inputs and review outputs in stages for higher quality and consistency.

Review exam-focused patterns for batch processing, multi-pass review, and structured output quality control.

Apply the section concepts by building a module that extracts structured information from customer return requests.

Learn how careful tool design helps Claude use tools safely, clearly, and effectively.

Review the Domain 2 objectives and understand what the exam expects around tools and MCP integration.

Learn how Claude selects tools, sends tool calls, receives results, and continues the agentic loop.

Review exam-specific details about tool execution and agentic loop behavior.

Learn how to design clear tool interfaces and return structured errors that Claude can understand and recover from.

Review exam-focused distinctions around tool schemas, tool interfaces, and error handling.

Understand how Claude accesses tools, how built-in tools differ from custom tools, and how MCP configuration fits in.

Learn how tools can be distributed and configured across systems using built-in tools and MCP patterns.

Apply the section by building backend tools and connecting them through an MCP-style integration.

Learn what makes an agentic system capable of working through tasks with less direct human control.

Review the Domain 1 objectives and understand the exam focus around agentic architecture and orchestration.

Learn how coordinators and subagents divide responsibilities and pass context across an agentic system.

Review exam-specific details about multi-agent orchestration and coordination patterns.

Learn how to break complex tasks into steps and control progress with hooks, gates, and handoffs.

Review exam-relevant details about enforcing workflow rules, decomposing tasks, and controlling execution.

Understand how session state, forks, scratchpads, and large context windows support complex agent workflows.

Review the key exam distinctions around session state, long-context usage, and workflow continuity.

Apply the domain by building the full ShopAssist AI agentic system using orchestration patterns from the section.

Learn how Claude Code can be configured to support different teams, projects, and development workflows.

Review the Domain 3 objectives and understand the exam focus around Claude Code configuration and workflows.

Learn how rules and memory guide Claude Code behavior across files, sessions, and team conventions.

Review exam-specific details about CLAUDE.md hierarchy, path-based rules, and configuration precedence.

Learn how Claude Code sessions can be resumed, compacted, or forked to manage development context.

Review exam-relevant patterns for working with large codebases and maintaining useful context.

Learn how commands, skills, and Plan Mode help structure Claude Code workflows.

Review exam-focused differences around commands, skills, planning, and iterative refinement.

Understand how Claude Code can support CI-CD workflows and independent review processes.

Review the exam-specific expectations for Claude Code integration into automated development pipelines.

Learn why reliability requires intentional system design, not just better prompts or stronger models.

Review the Domain 5 objectives and understand the exam focus around reliability and context management.

Learn how to design systems that escalate unclear, risky, or ambiguous cases instead of guessing.

Understand how errors move through multi-agent systems and how to prevent small failures from becoming larger issues.

Learn when to involve human reviewers and how confidence signals support safer decision-making.

Learn how to track where information comes from and synthesize multiple sources responsibly.

Connect reliability patterns back to the CCA-F exam and understand how they appear across final exam scenarios.