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Claude Certified Developer - Foundations Exam Prep: A Two-Hour Crash Course

Published by O'Reilly Media, Inc.

Intermediate content levelIntermediate

Walk the CCDV-F exam blueprint, drill practice questions across all domains, and leave with a personal study plan

What you’ll learn and how you can apply it

  • Decode the CCDV-F exam blueprint
  • Answer API mechanics questions with confidence
  • Choose models and manage cost
  • Apply the exam’s agent and tool decision frameworks
  • Defend Claude applications the way the exam expects
  • Build a personal study plan

Course description

In this hands-on two-hour course with Lucas Soares, you’ll prepare for the Claude Certified Developer – Foundations exam (CCDV-F), Anthropic’s certification for developers who build production applications, agents, and workflows on Claude. You’ll start by examining the CCDV-F blueprint and its eight weighted domains so you know exactly where your study time pays off. Then you’ll work domain by domain through the concepts the exam actually tests: Messages API mechanics, prompt caching and the Message Batches API, model tier selection, agent versus workflow architecture, the built-in-tool versus custom-tool versus Skill versus MCP decision, prompt injection defense, and Claude Code configuration. You’ll answer scenario-based practice questions via live polls as you go. By the end, you’ll have a domain-by-domain readiness map, a take-home cheat sheet of the decision frameworks the exam rewards, and a two-week study plan built on the official prep path and Anthropic documentation.

This live event is for you because...

  • You’re an AI/ML engineer who’s building on the Claude API and wants to certify your skills and needs a focused, blueprint-driven prep session.
  • You’re a senior software engineer or tech lead shipping Claude-powered features and you want an independent credential validating your API, agent, and MCP knowledge.
  • You’re a practitioner at an Anthropic partner firm where the Claude Certified Developer credential counts toward partner enablement and client credibility.

Prerequisites

  • Hands-on experience with Claude or a comparable LLM API
  • Working proficiency in Python and/or TypeScript, and fluency with REST APIs and CLI tools
  • Familiarity with LLM fundamentals (tokens, context windows, and tool/function calling)
  • A browser for the live polls and the course materials

Recommended preparation:

  • Download and skim the official CCDV-F Exam Guide PDF from the Anthropic Partner Academy certifications page
  • Create a free Anthropic Console account and generate an API key; install the SDK in your preferred language: pip install anthropic or npm install @anthropic-ai/sdk
  • (Optional) Install [Claude Code](http://npm install -g @anthropic-ai/claude-code) to follow the Claude Code configuration segment hands-on
  • Clone the course repo (link shared one week before the session)

Recommended follow-up:

Schedule

The time frames are only estimates and may vary according to how the class is progressing.

The exam, the blueprint, and your study strategy (15 minutes)

  • Presentation: What the CCDV-F exam is; the eight-domain blueprint as a study map; domain weights; where not to over-study; the scenario-question style: best-fit trade-offs, not trivia
  • Q&A

Applications, integration, and model optimization (40 minutes)

  • Presentation: Claude API mechanics the exam tests; messages API structure system prompts, multi-turn, stop reasons; streaming, vision, extended thinking, structured outputs; prompt caching and cache checkpointing; Message Batches API trade-offs; third-party access (Bedrock and Vertex differences); application design and configuration management; instruction interpretation across Claude Code, Desktop, claude.ai, API; CLAUDE.md files, settings.json, model version pinning, prompt versioning; model selection and cost management; Opus versus Sonnet versus Haiku—quality, latency, cost trade-offs; fast mode, adaptive thinking, effort levels; token budgeting
  • Hands-on exercises: Answer poll-based scenario questions on API mechanics; answer model-selection scenario questions
  • Q&A
  • Break

Agents, tools, and MCPs (30 minutes)

  • Presentation: Agent architecture the exam rewards; workflow versus agent decision criteria; supervisor hierarchies, subagents; Claude Agent SDK, custom loops, self-hosted versus Anthropic-hosted; hooks for deterministic actions; frameworks—Strands, LangGraph, PydanticAI; the agentic customization decision; built-in tools versus custom tools versus Skills versus MCP servers; tool description writing, approval patterns, client versus server tools; MCP server basics (resources, tools, prompts); stdio versus sockets
  • Hands-on exercises: Agent and tool scenario questions
  • Q&A

Prompting, context, security, and the small domains (25 minutes)

  • Presentation: Prompt and context engineering; system versus user placement, few-shot, output constraints; context drift and bloat (tool-output pruning, compaction, subagent isolation); output handling—structured output validation, defensive parsing; security and safety answers; prompt injection—untrusted input isolation, least-privilege guardrails; hooks as safety controls; secrets and key management; Claude Code, evals, and debugging in one pass; rules, skills, commands, agents, memory; headless and auto modes; trace analysis—isolating integration-layer versus model-output failures
  • Hands-on exercises: Mixed-domain rapid-fire questions
  • Q&A

Your readiness map and study plan (10 minutes)

  • Presentation: A take-home package (domain cheat sheets, decision-framework matrix, two-week study plan); mapping blueprint domains to the official five-course prep path and Anthropic docs; registration walkthrough
  • Q&A

Your Instructor

Lucas Soares

Lucas Soares is a machine learning engineer who has worked at K1 Digital and Biometrid, where he developed computer vision and NLP models for applications such as document verification, OCR-based applications, and recommender systems. Lucas has also developed various ML models, including neural networks, Siamese networks, convolutional neural networks, LSTMs, and genetic algorithms.

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Skills covered

  • Claude
  • QA / Testing