3-Day Live Instructor-Led Training
Hands-on Labs
After-Course Instructor Coaching Included
Exam Prep: Claude Certified Developer (CCDV-F)
Course 1357
- Duration: 3 days
- Language: English
- Level: Intermediate
This course prepares developers both to pass the CCDV-F (Claude Certified Developer — Foundations) exam and to apply those skills in creating AI-first, and AI-enabled business applications.
Participants will cover every exam domain with the time allocated to each topic matching the exam’s own domain weightings, ensuring no domain is either over- or under-represented. Every examinable concept and key skill is reinforced with hands-on experience, with at least 50% of each day spent building practical, AI applications. Participants leave with the knowledge and experience to architect agents and workflows, design efficient and effective AI applications, integrate AI resources using the Claude API, and implement security and guardrails and ready to tackle the CCDV-F certification exam. All the working code used in the course is also available to participants.
Each section in the course maps directly to CCDV-F exam domains (each one of which is essential to creating an effective AI application) and closes with exam-style checkpoint questions using the same scenario-based, multiple-choice format used on the real exam. The course finishes with a full mock exam and personalized readiness review for those participants who want to take advantage of it. The course also covers test-taking strategies and tactics including how to analyze exam questions to ensure that you are meeting the exam’s requirements.
Claude Developer Exam Prep Course Delivery Methods
In-Person
Online
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Claude Developer Exam Prep Course Information
Course Benefits
- Build and integrate production‑ready applications using Claude APIs and SDKs
- Select, optimize, and evaluate Claude models for cost‑effective performance
- Design and implement agentic workflows using Anthropic’s agent frameworks and MCP tools
- Engineer robust prompts, contexts, and safety‑aligned interaction patterns
- Evaluate, test, and debug Claude applications for reliability and safety
Prerequisites
Some experience with creating applications, including using command line tools and a code editor.
(Special Pricing Considerations/Competitor Pricing/Licensing Fees:
Courseware Cost: None
Exam Voucher NOT PROVIDED
Target Audience:
Developers and technical staff preparing for CCDV-F certification and/or creating AI-First or AI-Enabled applications. No prior experience with creating AI applications is required.
Claude Developer Exam Prep Course Outline
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
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Claude Developer Exam Prep Course FAQs
Candidates should have experience with software development and a working knowledge of generative AI concepts. Familiarity with Claude APIs, prompt and context engineering, tool use, agents, the Model Context Protocol (MCP), and techniques for testing and evaluating AI applications will also be beneficial.
While self-study can help you become familiar with certification topics, Learning Tree provides a structured, instructor-led experience designed to help you turn concepts into practical skills. You’ll learn from an expert instructor, work through complex topics and real-world scenarios, ask questions in real time, and benefit from a focused learning environment that keeps your preparation on track. The course also helps you identify knowledge gaps and focus on the skills and concepts most relevant to the certification exam—while building practical knowledge you can apply beyond the exam.