Claude Certified Architect CCAR-F+CCAR-P Masterclass

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Claude Certified Architect CCAR-F+CCAR-P Masterclass
Published 11/2026
Created by Vasco Patrício
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 195 Lectures ( 25h 18m ) | Size: 20.1 GB
A masterclass covering all CCAR-F/CCA-F and CCAR-P domains, from agents to integration, evals, governance, and more

What you'll learn

⚡ You will learn how to pass both Claude Certified Architect - Foundations (CCAR-F/CCA-F) an Claude Certified Architect - Professional (CCAR-P)
⚡ You will learn how to design production-grade orchestrated agentic systems, Claude Code workflows, and context engineering for fast-moving data environments
⚡ You will learn how to turn business needs into Claude architectures, and choose between augmented calls, workflows, agents, and multi-agent orchestration
⚡ You will learn about agent orchestration, context management, effective prompting, tool and MCP use, RAG pipelines, and Claude Code specific features
⚡ You will learn how to evaluate, A/B test, diagnose, and optimize Claude systems for accuracy, latency, cost, safety, and security
⚡ You will learn how to govern Claude systems in production with layered guardrails, human-in-the-loop validation, compliance controls, honest SLAs, and stakehold
⚡ You will learn about the technologies involved in both certifications, including Claude Code, the Claude API, Agent SDK, MCP, and more

Requirements

❗ A basic understanding of LLMs and agents is recommended (although we will recap)
❗ A basic understanding of Anthropic's offering (Claude Code, Claude API, etc.) is recommended (although we will recap)

Description

FROM FOUNDATIONS TO PROFESSIONAL: THE COMPLETE CLAUDE ARCHITECT PATH
The Claude Certified Architect - Foundations (CCAR-F/CCA-F) was the first professional certification focused on Anthropic's entire ecosystem and modern AI architecture. The Claude Certified Architect - Professional (CCAR-P) takes it a step further.
The CCAR-F asks: can you build a working Claude system correctly?
The CCAR-P asks: can you own it? From discovery and architecture, to integration, evaluation, governance, stakeholder communication, and continuous improvement.
Most prep material covers only one of these. Either the mechanics (stop_reason, hooks, CLAUDE .md, MCP), or the judgment (trade-offs, SLAs, compliance, evaluation gates). But a real production-grade Claude architect needs both. The mechanics without the judgment build fragile systems. The judgment without the mechanics stays on the whiteboard.
If only you could find a course that combined both...
This masterclass does exactly that. One structured path, from the five CCAR-F domains (or, as I call them, the Five Layers) all the way to the seven CCAR-P domains, with the architectural principles that connect them.
Along the way, you'll have practice exams, role plays, Labs (for UB Pro clients), and quizzes to consolidate your knowledge every step of the way.
You'll also have my Triangle Prep strategy for identifying and avoiding exam traps, such as tricky language, "sophistication baiting" and other traps that Anthropic loves to place on their exams... and how to pass them!
PART 1: CCAR-F (THE FIVE LAYERS)
✨ Agentic Architecture & Orchestration (the Agentic Layer)
✨ Tool Design & MCP Integration (the Tool Layer)
✨ Claude Code Configuration & Workflows (the Workflow Layer)
✨ Prompt Engineering & Structured Output (the Prompt Layer)
✨ Context Management & Reliability (the Context Layer)
PART 2: CCAR-P (THE SEVEN DOMAINS)
✨ Solution Design & Architecture (Architecture, 17%): turning business problems into end-to-end Claude architectures
✨ Claude Models, Prompting & Context Engineering (Curation, 13%): selecting model tiers, prompts, and context efficiently
✨ Integration (19%, the largest domain): RAG, MCP vs. APIs vs. agent-to-agent, tool scoping, and observability
✨ Evaluation, Testing & Optimization (Improvement, 16%): proving a system works, and keeping it working
✨ Governance, Safety & Risk Management (Oversight, 14%): guardrails, human review, compliance, and responsible AI
✨ Stakeholder Communication & Lifecycle Management (Stewardship, 14%): discovery, trade-offs, SLAs, and documentation
✨ Developer Productivity & Operational Enablement (Team Tooling, 7%): configuring Claude Code for teams, workflows, and debugging
We'll start each part by covering the certification itself: the exam structure, weighting, scoring, and tested competencies (including the CCAR-P's 63 scenario-based questions, 120 minutes, and 720/1000 passing score). From there, we'll explore every domain in detail, and close out with the cross-domain patterns, the common traps and antipatterns, and the key takeaways that summarize each certification.
Always focusing not on isolated features, but on true production-grade architectural thinking.
THE MEGALIST: EVERYTHING YOU WILL LEARN IN THIS COURSE
Nothing hidden. Here is everything inside the package.
CCAR-F: Agentic Architecture & Orchestration
✨ Design agentic loops and stop_reason-based workflow control;
✨ Coordinate multi-agent systems, specialized subagents, and context handoffs;
✨ Spawn and manage subagents using Agent SDK concepts, in sequential and parallel workflows;
✨ Implement workflow safeguards using hooks, and manage session persistence, resumption, and forking;
CCAR-F: Tool Design & MCP Integration
✨ Design effective tools, parameter schemas, and

Description

s that improve routing accuracy;
✨ Design structured error responses and recovery strategies;
✨ Understand MCP architecture (resources, prompts, and tools) and configure it securely across team and personal scopes;
✨ Apply least-privilege principles and master Claude Code's built-in toolset;
CCAR-F: Claude Code Configuration & Workflows
✨ Configure CLAUDE .md hierarchies, precedence rules, and path-specific rules;
✨ Build reusable skills and custom commands, using context: fork and tool restrictions;
✨ Choose between Plan Mode and Direct Execution, and design iterative refinement workflows;
✨ Integrate Claude Code into CI/CD pipelines with machine-readable outputs;
CCAR-F: Prompt Engineering & Structured Output
✨ Write prompts with explicit criteria, classification frameworks, and evaluation rubrics;
✨ Apply few-shot prompting with high-quality edge-case examples;
✨ Enforce structured outputs using schemas, tool use, nullable fields, and validation-retry loops;
✨ Use the Batch API and design multi-pass, independent review systems;
CCAR-F: Context Management & Reliability
✨ Understand context windows, token economics, fact blocks, and layered memory;
✨ Mitigate lost-in-the-middle effects and progressive summarization failures;
✨ Propagate errors through multi-agent systems with graceful degradation and recovery;
✨ Use scratchpads, delegated exploration, provenance, and confidence-calibrated human review;
CCAR-P: Architecture
✨ Translate vague business requests into dominant architectural problems tied to business value;
✨ Use stage gates to enforce validation, approvals, policies, and risk thresholds in code;
✨ Choose between augmented calls, workflows, and agents based on predictability, autonomy, and problem decomposition;
✨ Design coordinators that delegate bounded tasks, pass curated context, reconcile outputs, and recover at the right level;
CCAR-P: Curation
✨ Select the least expensive Claude model tier that reliably meets quality, latency, context, and budget needs;
✨ Defend model choices and migrations with evaluation evidence, reasoning effort, regression testing, and staged rollouts;
✨ Diagnose prompt failures and apply zero-shot, few-shot, chain-of-thought, or self-consistency only when justified;
✨ Assemble context through selection, positioning, structured facts, compression, prompt caching, and progressive disclosure;
CCAR-P: Integration
✨ Design RAG pipelines with chunking, indexing, and retrieval strategies matched to data shape, query patterns, freshness, and attribution;
✨ Choose between MCP, direct APIs/CLI, and agent-to-agent integrations based on reuse, latency, ownership, and security boundaries;
✨ Evaluate progressive discovery vs. monolithic context strategies, and spot capability bloat;
✨ Apply validated schemas, least privilege, authentication, and acting-user authorization to tool use;
✨ Balance accuracy, latency, and cost through observability, structured errors, targeted recovery, and graceful degradation;
CCAR-P: Improvement
✨ Define evaluation metrics across accuracy, latency, cost, safety, and security, with code-based and model-based grading;
✨ Build representative evaluation datasets, explicit thresholds, and acceptance gates;
✨ Run A/B tests without overclaiming, and tell apart prompt failures, hallucinations, model mismatches, and integration errors;
✨ Use failure signatures and structured incident records to drive architectural changes;
✨ Model and optimize production cost and latency with pilots, caching, routing, batching, and monitoring;
CCAR-P: Oversight
✨ Layer guardrails across model, input, tool, process, and human controls;
✨ Constrain behavior with deterministic enforcement hooks, structural isolation, and capability removal;
✨ Place human-in-the-loop review by consequence, objective escalation triggers, calibrated confidence, risk, and novelty;
✨ Turn regulations (GDPR, HIPAA, FedRAMP) into concrete controls backed by defensible evidence;
✨ Address fairness, bias, transparency, and vendor governance in responsible AI;
CCAR-P: Stewardship
✨ Run structured discovery that turns business, legal, security, and operational needs into explicit

Requirements

;
✨ Expose hidden assumptions and make vague language observable through metrics and acceptance criteria;
✨ Frame trade-offs across Capabilities, Cost and Speed, and Safety and Security;
✨ Write honest SLAs that separate hard infrastructure guarantees from measured quality commitments;
✨ Preserve ownership through documentation, handoffs, decision logs, monitoring, triage, and iteration;
CCAR-P: Team Tooling
✨ Configure Claude Code instruction scopes, conditional rules, imports, settings precedence, and allowlisting for teams;
✨ Distribute configuration centrally, through repositories, or per individual, and weigh consistency vs. flexibility;
✨ Build dependable workflows with bounded contexts, restricted tools, risk-based planning, and verification;
✨ Debug non-interactive automation through structured outputs, observable boundaries, and systematic failure isolation;
Cross-Domain Architecture Patterns
✨ Apply recurring patterns: independent checking, local resolution, constraint-led trade-offs, staleness management, and auditable triggers;
✨ Avoid the classic exam traps: over-provisioning, false determinism, ignored reversibility, unproven confidence, and proxy substitution;
✨ Justify every trade-off with evidence, not intuition, and develop the architectural mindset expected of a Claude Certified Architect;
Whether you're preparing for the CCAR-F, the CCAR-P, or both, building Claude systems professionally, or just looking to deepen your understanding of modern AI architecture... this course gives you a structured path from fundamentals all the way to professional-level architectural judgment.

Who this course is for

⭐ Developers, tech leads, and solutions architects integrating Claude Gen AI into their applications
⭐ Senior solution architects, AI/ML engineers, and platform engineers owning production Claude systems end to end
⭐ IT experts integrating Gen AI into existing solutions, infrastructure, or regulated environments
⭐ Any professional curious about how to design, govern, and scale production-ready applications using Claude

Homepage

https://www.udemy.com/course/ccarf-ccarp

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Who this course is for

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