Study Method - AI Cert Prep

Principle 1 – Study weighted, not flat

The most common self-sabotage is spending equal time on a 2.6% domain and a 33.1% domain. Convert weights into hours:

Example for CCDV-F with a 30-hour budget:

Domain	Weight	Hours
Applications and Integration	33.1%	~10
Model Selection and Optimization	16.8%	~5
Agents and Workflows	14.7%	~4.5
Prompt and Context Engineering	11.0%	~3.5
Tools and MCPs	10.6%	~3
Security and Safety	8.1%	~2.5
Claude Code	3.1%	~1
Eval, Testing, and Debugging	2.6%	~1

Then redistribute toward your measured weak domains after a first practice exam.

Principle 2 – Build while you study

The official guides say it directly: combine study with hands-on work. One weekend project that touches five domains beats ten more hours of reading. Suggested builds per exam:

Exam	Minimum viable build
CCAO-F	Configure a Claude Project with custom instructions and 3–5 knowledge documents; run one real workflow; evaluate the outputs for accuracy, bias and audience fit; write a one-page adoption memo
CCDV-F	An app that calls the Messages API (streaming + error handling), uses one custom tool and one MCP server, applies prompt caching, and includes a 20-case eval
CCAR-F	A coordinator/subagent research system with structured JSON output, a validation-retry loop, hooks for a hard business rule, and a CLAUDE.md hierarchy for a repo
CCAR-P	Everything in CCAR-F plus a RAG pipeline with chunking choices you can defend, an eval harness with A/B comparison, observability dashboards, and an architecture decision record (ADR) set

Principle 3 – Learn the trade-off, not the fact

Exam items give a scenario and four defensible options. The correct one reflects the right trade-off given the constraints in the stem. For every concept, be able to answer:

When is this the right choice?
What signal in a scenario points to it? (“cost matters, latency does not” → Batch API)
What is the tempting wrong alternative, and why is it wrong here?

The domain pages on this site are written around this triad.

Principle 4 – Use practice exams as a booking gate

Fees are $99–$175 per attempt and retake waits are 14–90 days. A failed attempt costs money and months. Decision rule:

Score ≥ 80% on a fresh, timed practice exam, with no domain below 65% → book.
Otherwise → redistribute hours to the weak domains, rebuild, re-test.

A six-week template (5–8 h/week)

Week	Focus
1–2	Read your course in weight order; do the free Anthropic Academy courses mapped to your exam
3–4	Build the minimum viable project; revisit domain pages as questions arise
5	Timed practice exam; drill the weak domains with the domain-level practice questions
6	Close gaps, re-test, review exam-day policy, book with the 24/48-hour change window in mind

Free Academy courses mapped to each exam

Exam	Highest-value free courses (Anthropic Academy on Skilljar)
CCAO-F	Claude 101 · AI Capabilities and Limitations · AI Fluency track for your role
CCDV-F	Building with the Claude API (the backbone) · Introduction to Model Context Protocol · Claude Code in Action · Introduction to Agent Skills · Introduction to Subagents
CCAR-F	Building with the Claude API · MCP: Advanced Topics · Claude Code 101 · official docs on agents, context management and tool design
CCAR-P	Everything in the CCAR-F row plus Claude on Amazon Bedrock / Google Vertex AI if you deploy through a cloud platform

Reading the official documentation efficiently

Documentation was split in July 2026:

Claude API and platform: platform.claude.com/docs/en/*
Claude Code: code.claude.com/docs/en/*
Agent SDK: code.claude.com/docs/en/agent-sdk/* (renamed from “Claude Code SDK” to Claude Agent SDK)
MCP specification: modelcontextprotocol.io

Old docs.anthropic.com URLs still redirect. When a study source and the live docs disagree, the live docs win.