Anthropic Claude Certified Architect Professional (CCAR-P) Cert Prep

10h 8mAdvanced2026-09-14

Authors

Tutorials Dojo

Course details

This course is designed for mid- to senior-level technical professionals aiming to excel in the Anthropic Claude Certified Architect Professional (CCAR-P) exam. Discover essential topics such as solution design and architecture, covering the translation of business problems into actionable AI solutions. Learn about Claude models, prompting techniques, and context engineering to elevate your technical skills. Explore integration strategies that allow the seamless delivery of AI systems across production environments. Examine the methodologies behind rigorous evaluation, testing, and optimization practices. Review governance, safety, and risk management to build systems that comply with regulatory standards. Enhance your stakeholder communication abilities to ensure lifecycle management within AI projects. This course is ideal for experienced solution architects, AI/ML engineers, and senior software engineers seeking a robust understanding of AI-driven solution delivery.

Concepts

Introduction

Claude Certified Architect Professional (CCAR-P) Overview

Architecture Patterns and Design

Choosing an agent framework (Strands vs. LangGraph vs. PydanticAI)
Invoking Claude through third-party vendors (Bedrock, Vertex)
What is a task decomposition
Decomposing a complex request into steps
Using Claude to analyze requirements and identify use cases
Communicating Claude's value and limitations to stakeholders
The architecture of agentic AI
Agentic AI vs. generative AI
What is LangGraph
What is AWS Strands Agents
Types of agentic AI

The Agentic Loop

What is Claude
What is the agentic loop
Understanding the stop reason field
How to Branch on tool use
How to append tool results to history
How to continue the loop correctly
Common mistake - Skipping history on tool results
Common mistake - Ending the loop too early
Memory and context in AI agents

Hub-Spoke Model

Introduction to the hub-spoke model
How communication flows in hub-spoke
How a coordinator decomposes tasks
How a coordinator invokes subagents
How to pass context to a subagent
How a coordinator aggregates results
How a coordinator handles subagent errors
Why subagents have no memory of the coordinator
How to Diagnose Incomplete Coordinator Output
Multi-agent workflows with Claude
Multi-agent systems - When AI agents work together

Task Tool, Parallel Execution, and Programmatic Control

Introduction to the Task tool
Why coordinators need Task tool in allowedTools
How to Run Subagents in Parallel
When to use sequential vs. parallel invocation
What is the Stakes-Proportionate Rule
What is a Programmatic Gate
Introduction to Tool Call Interception Hooks
Introduction to PostToolUse hooks
Why prompts alone cannot guarantee correctness
What is a Claude hook

Model Selection

What are the Claude models
What are temperature and sampling
What is time to first token (TTFT)
Choosing the Right Claude Model
What is non-determinism in LLMs
What is next-token generation
What are breaking behavior changes across model releases
What is model version pinning

Prompt Design and Guardrails

What is prompt versioning
System Prompts & Custom Personas
What is zero-shot prompting
What is few-shot prompting
What is Chain-of-Thought Prompting
Designing System Prompts and Guardrails
Selecting a prompting technique
What are zero-shot, single-shot, and multi-shot prompting
How many few-shot examples to include
When to reach for few-shot examples

Context, Tokens, and Prompt Reuse

What is a context window
What are tokens and token usage
What is prompt caching
What is prompt prefix ordering
What are modular prompts and skills
Managing the context window and token budget
How progressive summarization loses detail
What is a case facts block
What is the lost-in-the-middle problem
How to Place Key Findings at the Top of Context
How structured tags improve reliability
How to Trim Verbose Tool Results

Integration Protocols and Context Strategy

What goes in project-level Model Context Protocol (MCP) configuration
What Goes in User-Level MCP Config
How to Diagnose Missing MCP Server Access
Why having too many tools hurts selection
How Many Tools Each Agent Should Have
How to Scope Tools to Agent Roles
Retrieval-Augmented Generation (RAG) with Claude
What are secrets and application programming interface (API) key management
Layering guardrails and applying least privilege
What is an MCP Server
Agent Communication Protocol (ACP)
Agent2Agent (A2A)
What is MCP (Model Context Protocol)
What is API CLI integration
What is agent-to-agent (A2A) communication

Performance and Observability

What is a hallucination
Structured error context in multi-agent systems
Why alternative approaches matter most
Common mistake - Waiting until retries are exhausted
How to Choose Between Cost and Latency
What are the common error types in Claude apps
Monitoring production quality
Debugging - isolating integration-layer vs. model-output problems
What is a failure mode
Selecting an error-recovery strategy
What is LangSmith

Safety, Risk, Red Teaming, and Compliance

What is prompt injection
Defending against prompt injection and jailbreaks
Using hooks to block destructive actions
What Is Data Sensitivity and Personally Identifiable Information (PII)
What are authentication, authorization, confidentiality, privacy, and integrity
What are content policy and guardrail layering
What Is Secure-by-Design

Human-in-the-Loop and Ethical AI

When a Direct Human Request is a Valid Trigger
When an Authority Gap is a Valid Trigger
When a Verification Flag is a Valid Trigger
Why frustration alone is not a valid trigger
Why Complexity Alone Is Not a Valid Trigger
How to Handle a Frustrated Customer with a Simple Issue
When to escalate immediately without investigating
Human-in-the-loop (HITL) validation patterns
Governance and Compliance in AI

Solution Lifecycle and Service-Level Agreement (SLA) Management

What is an SLA (service-level agreement)
What is a stakeholder feedback loop
What is architecture documentation and implementation guidance
What are the AI solution lifecycle phases (discovery, design, handoff, monitoring, and iteration)
Managing configuration - CLAUDE.md, settings.json, version pinning, and prompt versioning

CLAUDE.md Hierarchy

Introduction to the CLAUDE.md Hierarchy
What goes in project-level CLAUDE.md
What Goes in Directory-Level CLAUDE.md
What Goes in User-Level CLAUDE.md
How to diagnose ignored team conventions

Agent Skills

CLAUDE.md vs. agent skills - How loading differs
Where universal standards belong
Where task-specific workflows belong
Introduction to Skills
Understanding How Skills Are Created and Work
Picking the right tool - Skills compared to everything else
Advanced skills - Configuration and scaling

Path-Specific Rules and Command-Line Interface (CLI)

What is context - fork
When to use context - fork
Introduction to path-specific rules
Path-specific rules vs. directory CLAUDE.md
How path-specific rules affect the token budget
What the -p flag does
How to run Claude Code in continuous integration and continuous delivery (CI CD)
What is Claude Code
What are Claude Code's core components
What are slash commands
What are headless, streaming, and auto modes