Exam Prep: Claude Certified Developer (CCDV-F)

Day 1 - Applications and Integration

Requirements & Systems Lifecycle

• Translating functional/business requirements into Claude‑powered application designs
• Systems lifecycle concepts: design → build → deploy → monitor
• Version control workflows (Git), code review practices
• Configuration management: CLAUDE.md, settings.json, plugin dependencies

Claude API Mechanics

• Messages API (roles, content blocks, tool calls)
• Streaming responses
• Vision inputs
• Thinking mode
• Caching API
• Batches API
• Third‑party invocation patterns
• Error handling and retry strategies

Software Engineering Foundations

• REST API fundamentals
• JSON schemas and structured output validation
• Async programming patterns (Python / TypeScript)
• Dependency management and environment setup
• Logging, observability, and monitoring Claude applications

Claude Application Design

• Designing multi‑interface Claude apps (claude.ai, Desktop, API, SDKs)
• Multi‑modal input handling (text, images, code)
• State management and context window budgeting
• Model version pinning and upgrade strategies

Day 2 - Model Selection and Optimization

LLM Fundamentals

• Tokens, tokenization, and context windows
• Sampling parameters (temperature, top‑p, top‑k)
• Determinism vs. non‑determinism
• Latency and throughput considerations

Model Tier Selection

• Capability tiers: Haiku, Sonnet, Opus, Fable (emerging)
• Choosing models based on task complexity, cost, and latency
• Structured output reliability across model tiers

Optimization Techniques

• Token‑efficient prompt design
• Caching strategies
• Batch processing for large workloads
• Cost management and monitoring

Agents and Workflows

Agent Architecture

• When to use agents vs. workflows
• Anthropic Agent SDK fundamentals
• Custom agent loops and harnesses
• Hosted vs. self‑hosted agent deployments

Agent Construction

• Building agents with Claude Agent SDK
• Memory management and context window strategies
• Tool‑use loops and multi‑step reasoning
• Sub‑agents and delegation patterns

Agent Patterns & Frameworks

• Strands
• LangGraph
• PydanticAI
• Common agentic design patterns (planner‑executor, router‑selector, tool‑first loops)

Day 3 - Prompt and Context Engineering

Prompt Engineering

• Instruction hierarchy and role prompting
• System vs. user vs. developer messages
• Multi‑turn prompt stability
• Guardrails through prompt structure

Context Engineering

• Context drift prevention
• Retrieval‑augmented context injection
• Chunking strategies for long documents
• Maintaining state across multi‑step workflows

Output Handling

• Enforcing structured output (JSON, XML, custom schemas)
• Validation and repair loops
• Using tool calls to constrain output

Tools and MCPs

Tool Implementation

• Tool schemas and function definitions
• Input/output validation
• Error handling inside tools
• Multi‑tool orchestration

MCP (Model Context Protocol)

• MCP server architecture
• Secure tool exposure
• Hooks and guardrails
• Defensive patterns against malicious or unsafe tool invocation

Custom Tools & Skills

• Custom tool development
• Integrating Skills with Claude Code
• Plugin dependencies and configuration

Security and Safety (8.1%)

Application Security

• Secrets and key management
• Secure API usage
• Environment isolation
• Logging and redaction of sensitive data

Prompt Injection Defense

• Injection patterns and detection
• Guardrail hooks
• Safe tool invocation
• Preventing destructive actions

Safety Principles

• Anthropic safety guidelines
• High‑risk request handling
• Model fallback behavior (e.g., classifier safeguard layers)

Claude Code

Core Components

• Rules
• Skills
• Commands
• Agents
• Agent Memory

Interaction Modes

• Session management
• Slash commands
• Headless mode
• Streaming mode
• Auto‑mode

Project Structure

• CLAUDE.md hierarchy
• Repository initialization
• settings.json configuration
• Plugin dependencies

Eval, Testing, and Debugging (2.6%)

Evaluation

• Designing evals
• Structured output validation
• Quality monitoring in production
• Regression testing for prompts and agents

Debugging

• Identifying error types (integration vs. model output)
• Trace analysis
• Failure isolation
• Recovery strategies

Testing

• Unit tests for tools and MCP servers
• Integration tests for agent workflows
• Load testing and stress scenarios