Claude Certified Architect – Foundations
Certification Covered
CCA-F Certification Training
Stack
Program Includes:
Description
The Claude Certified Architect – Foundations (CCA-F) certification training is a comprehensive 32-hour program designed to validate deep, hands-on proficiency with the Anthropic Claude ecosystem. The training covers five core domains spanning agentic architecture, tool and MCP integration, Claude Code configuration, prompt engineering, and context management. Learners engage with realistic scenario-based labs and prepare for a rigorous certification exam that tests applied knowledge across all domains.
Course Outcomes
Upon successful completion of this training and certification, learners will be able to:
- Design and implement agentic loop architectures using the Claude Agent SDK, applying model-driven control flow, stop reason handling, and multi-agent coordinator-subagent patterns for complex workflow orchestration.
- Build and configure multi-agent systems with explicit context passing parallel subagent execution, programmatic workflow enforcement, and hook-based interception using the Claude Agent SDK.
- Design and integrate MCP tools with precise tool descriptions, structured error handling, scoped tool distribution, and tool choice configuration to optimize LLM tool selection accuracy.
- Configure Claude Code projects using CLAUDE.md hierarchy, custom slash commands, Skills, path-specific rules, and CI/CD integration to build reliable, team-ready development workflows.
- Apply advanced prompt engineering techniques including explicit criteria design, few-shot prompting, structured output via tool use and JSON schema, retry-with-feedback loops, and multi-pass review architectures.
- Implement context management strategies for large-scale agent workflows including context preservation, escalation patterns, error propagation handling, and information provenance with uncertainty tracking.
- Evaluate and optimize agent system reliability by applying confidence calibration, human-in-the-loop routing, field-level validation, and structured claim-source mapping for trustworthy production deployments.
Prerequisites
- Familiarity with REST APIs and JSON/JSON Schema
- Working knowledge of at least one programming language (Python or JavaScript/TypeScript recommended)
- Basic understanding of CI/CD pipeline concepts
- Familiarity with command-line tools and version control (Git)
- Understanding of LLM fundamentals including prompting, context windows, and tokens
Course Content
1Agentic Architecture & Orchestration7 Sections
1.1 - Agentic Loop Design using Claude Agent SDK
1.1 - Agentic Loop Design using Claude Agent SDK
1.2 - Multi-Agent Orchestration (Coordinator-Subagent Pattern)
1.2 - Multi-Agent Orchestration (Coordinator-Subagent Pattern)
1.3 - Subagent Invocation & Context Passing
1.3 - Subagent Invocation & Context Passing
1.4 - Multi-Step Workflows & Enforcement Patterns
1.4 - Multi-Step Workflows & Enforcement Patterns
1.5 - Hooks & Interception using Claude Agent SDK
1.5 - Hooks & Interception using Claude Agent SDK
1.6 - Task Decomposition Strategies
1.6 - Task Decomposition Strategies
1.7 - Session Management & State Handling
1.7 - Session Management & State Handling
2Tool Design & MCP Integration5 Sections
2.1 - Tool Interface Design & Selection Behavior
2.1 - Tool Interface Design & Selection Behavior
2.2 - Structured Error Handling in MCP Tools
2.2 - Structured Error Handling in MCP Tools
2.3 - Tool Distribution & tool_choice Configuration
2.3 - Tool Distribution & tool_choice Configuration
2.4 - MCP Server Integration with Claude Code & Agents
2.4 - MCP Server Integration with Claude Code & Agents
2.5 - Built-in Tools Usage in Claude Code
2.5 - Built-in Tools Usage in Claude Code
3Claude Code Configuration & Workflows6 Sections
3.1 - CLAUDE.md Configuration Hierarchy & Modularity
3.1 - CLAUDE.md Configuration Hierarchy & Modularity
3.2 - Custom Slash Commands & Skills
3.2 - Custom Slash Commands & Skills
3.3 - Path-Specific Rules & Conditional Loading
3.3 - Path-Specific Rules & Conditional Loading
3.4 - Plan Mode vs Direct Execution
3.4 - Plan Mode vs Direct Execution
3.5 - Iterative Refinement Techniques
3.5 - Iterative Refinement Techniques
3.6 - CI/CD Integration with Claude Code
3.6 - CI/CD Integration with Claude Code
4Prompt Engineering & Structured Output6 Sections
4.1 - Designing Prompts with Explicit Criteria
4.1 - Designing Prompts with Explicit Criteria
4.2 - Few-Shot Prompting for Consistency & Generalization
4.2 - Few-Shot Prompting for Consistency & Generalization
4.3 - Structured Output using tool_use & JSON Schema
4.3 - Structured Output using tool_use & JSON Schema
4.4 - Validation, Retry & Feedback Loops
4.4 - Validation, Retry & Feedback Loops
4.5 - Batch Processing with Claude API
4.5 - Batch Processing with Claude API
4.6 - Multi-Instance & Multi-Pass Architectures
4.6 - Multi-Instance & Multi-Pass Architectures
5Context Management & Reliability6 Sections
5.1 - Context Preservation & Optimization
5.1 - Context Preservation & Optimization
5.2 - Escalation & Ambiguity Resolution Patterns
5.2 - Escalation & Ambiguity Resolution Patterns
5.3 - Error Propagation in Multi-Agent Systems
5.3 - Error Propagation in Multi-Agent Systems
5.4 - Context Management in Large Codebase Exploration
5.4 - Context Management in Large Codebase Exploration
5.5 - Human Review & Confidence Calibration
5.5 - Human Review & Confidence Calibration
5.6 - Information Provenance & Uncertainty Handling
5.6 - Information Provenance & Uncertainty Handling
Overview of Capstone Projects
Intelligent Research & Report Generation System
Intelligent Research & Report Generation System
Candidates will design and implement a multi-agent research system that accepts a high-level research topic, autonomously decomposes it into sub-questions, retrieves and synthesizes information across multiple sources via MCP tools, and produces a structured, citation-backed report with confidence scores and provenance tracking. The system must be configurable via Claude Code, deployable in a CI/CD pipeline, and capable of human-in-the-loop escalation for low-confidence findings.
Deliverables
- Source code repository containing the full multi-agent system, CLAUDE.md hierarchy, .mcp.json, custom slash commands, and Skill definitions.
- A completed sample report generated by the system for a provided test research topic, including provenance metadata and confidence scores.
- CI/CD pipeline configuration (YAML) demonstrating non-interactive Claude Code execution with structured JSON output and automated validation step.
- A written Architecture Decision Record (ADR) (500–800 words) justifying key design choices across all five domains and reflecting on trade-offs encountered.
- A peer review checklist (provided template) completed for one other candidate’s submission, assessing domain coverage, correctness, and production readiness.
Evaluation Rubric
- Agentic Architecture (25%) — Correct coordinator-subagent decomposition, stop_reason handling, hook usage, and session management.
- Tool Design & MCP Integration (20%) — Precise tool descriptions, structured error handling, scoped distribution, and secure credential management.
- Claude Code Configuration (20%) — Functional CLAUDE.md hierarchy, working slash command and Skill, and successful CI/CD pipeline integration.
- Prompt Engineering & Structured Output (20%) — Few-shot example quality, schema correctness, retry loop implementation, and multi-pass review design.
- Context Management & Reliability (15%) — Provenance tracking completeness, escalation logic correctness, error propagation handling, and context optimization evidence.
Tech Stack
Career Opportunities
Learning Roadmap
Agentic Architecture & Orchestration
7 Sections
Tool Design & MCP Integration
5 Sections
Claude Code Configuration & Workflows
6 Sections
Prompt Engineering & Structured Output
6 Sections
Context Management & Reliability
6 Sections
Certified Claude Certified Architect – Foundations
Goal Achieved
Weekdays (Mon-Fri)
Starting from 16th March
Duration: 1.5 Hours per day
Weekend (Sat & Sun)
Starting from 21st March
Duration: 4 Hours per day
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Meet Our Leaders
Program Director
Set the direction of the program and ensure it meets industry and academic excellence standards.
Aashish Pandey
Blue Data Consulting
Program Manager
Manages schedules, coordination, assessments, and ensures smooth course delivery.
Ragini Sankhwar
Blue Data Consulting
Industry Expert/Trainers
Harsh Dalal
Blue Data Consulting
AI Consultant
SME
Covers theory, hands-on labs, assessments, and daily assignments.
Aproova
Blue Data Consulting
Master SME
Specialized guidance for Capstone Projects and advanced implementation.
Ahmed
Blue Data Consulting
Mentor
Dedicated support to help all learners pass the certification exam.
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Application Fee
5% of Total Fee (Non-refundable)
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Frequently Asked Questions
What is the CCA-F certification?
The Claude Certified Architect – Foundations (CCA-F) certification training is a comprehensive 32-hour program designed to validate deep, hands-on proficiency with the Anthropic Claude ecosystem.
What core domains are covered in the training?
The training covers five core domains:
- Agentic architecture
- Tool and MCP integration
- Claude Code configuration
- Prompt engineering
- Context management
What is the format of the exam?
The exam consists of multiple-choice questions. Each question features one correct answer and three distractors. There is no penalty for guessing.
What score do I need to pass?
Candidates must achieve a scaled score of 720 out of 1,000 to pass the certification exam.
Who is the target audience for this certification?
The target audience includes:
- Software engineers, AI engineers, and solution architects with 6 or more months of hands-on experience building with Claude APIs, Claude Agent SDK, Claude Code, and MCP integrations.
- Technical leads and platform engineers responsible for designing and deploying production-grade agentic systems, multi-agent workflows, and Claude-powered developer tooling.
- DevOps and MLOps engineers integrating Claude Code into CI/CD pipelines and seeking to validate advanced configuration, prompt engineering, and context management proficiency.
What are the prerequisites to take this course?
The prerequisites include:
- Familiarity with REST APIs and JSON/JSON Schema.
- Working knowledge of at least one programming language, with Python or JavaScript/TypeScript recommended.
- A basic understanding of CI/CD pipeline concepts.
- Familiarity with command-line tools and version control (Git).
- Understanding of LLM fundamentals including prompting, context windows, and tokens.
What are the technical lab requirements?
To participate in the labs, you will need:
- An Anthropic API key with access to Claude models, specifically claude-sonnet-4-6 recommended.
- Claude Code CLI installed and configured.
- A Node.js or Python development environment.
- A Git repository for Claude Code configuration exercises.
- At least one MCP server for integration practice.
- A CI/CD pipeline environment for Domain 3 labs.
- A code editor with terminal access.
- Access to Anthropic documentation at docs.claude.com.
How many scenarios are tested on the exam?
There are 6 total scenarios available, but you will only be tested on 4 scenario sets presented at random during the exam.
What is the capstone project?
The capstone project requires candidates to build an Intelligent Research & Report Generation System. You must design and implement a multi-agent research system that accepts a topic, autonomously decomposes it, retrieves information via MCP tools, and produces a structured, citation-backed report.
How is the capstone project evaluated?
The evaluation rubric is broken down into five categories:
- Agentic Architecture accounts for 25%.
- Tool Design & MCP Integration accounts for 20%.
- Claude Code Configuration accounts for 20%.
- Prompt Engineering & Structured Output accounts for 20%.
- Context Management & Reliability accounts for 15%.
What deliverables are expected for the capstone?
Candidates are expected to deliver:
- A source code repository containing the full multi-agent system, CLAUDE.md hierarchy, .mcp.json, custom slash commands, and Skill definitions.
- A completed sample report generated by the system for a provided test research topic.
- CI/CD pipeline configuration demonstrating non-interactive Claude Code execution.
- A written Architecture Decision Record (ADR) between 500 and 800 words justifying key design choices.
- A peer review checklist completed for one other candidate’s submission.
Where can I request exam access or view reference materials?
You can request official access to the training at anthropic.skilljar.com/claude-certified-architect-foundations-access-request. For reference materials, you can visit the Anthropic Documentation at docs.claude.com.