One preparation path per certification, built on the official exam guides: what each domain tests, which official material covers it, what to build, and the reasoning behind every schedule. Everything comes from official exam guides and certification pages — no exam-day accounts, no leaked questions.
Most prep guides start at chapter one. This one works backwards: derive the exam date from each vendor's retake rules (AWS makes you wait 14 calendar days, Google escalates 14 → 60 → 365 days, Microsoft allows a retake after 24 hours, and Taiwan's iPAS has no retake at all — you wait six months for the next session), then order your study by domain weight rather than domain number, and take a free official practice set before studying anything. Includes weightings for seven certifications and a five-week AIF-C01 example.
The AIF-C01 exam guide moved to v1.1 on April 30, 2026, adding seven objectives — MCP, multi-agent patterns, context engineering, token-based pricing, and hallucination detection all became testable, with Bedrock AgentCore, Kiro, and Strands Agents joining in-scope services. Almost every summary online describes the older version. This guide builds on the official five-domain weighting, integrating AWS's four-step prep method and field-tested advice from 10+ candidates who passed. $100, 90 minutes, 65 questions (50 scored), pass at 700, valid 3 years — and it is offered in Traditional Chinese.
AIP-C01 tests integrating foundation models into production AWS applications: RAG, agents, security, cost, and evaluation. This guide follows the five official domains with a prerequisite self-check, four-step preparation plan, resource choices, and hands-on checkpoints, plus a ten-week schedule, an AI study prompt, and exam-day guidance. Start with a diagnostic, then close gaps through one RAG-and-agent project.
Google's Professional ML Engineer exam guide was rewritten in 2026: Vertex AI is renamed Gemini Enterprise Agent Platform throughout, so older study material no longer matches the product names in the questions. This guide uses the official six-section weighting as its skeleton, listing what each section tests, which official materials cover it, and what to build — plus a study schedule whose reasoning is spelled out. Official specs: $200, two hours, 50–60 multiple-choice and multiple-select questions, two-year validity, 3+ years of industry experience recommended including 1+ year on Google Cloud.
A complete study guide for Claude's official architect certification (CCAR-F): five domains weighted 27/18/20/20/15, four scenarios drawn from six, common anti-patterns, and hands-on preparation. Official specs are 60 items / 120 minutes / $125 / 12-month validity / 720 to pass; registration is limited to Claude Partner Network members, and on-time renewal is free and non-proctored.
AI-103 replaces AI-102, retired June 30, 2026, and the objectives were rewritten around Microsoft Foundry — prompt flow, Azure AI Studio, Azure OpenAI Service, and Azure AI Agent Service appear nowhere in them. The five skill areas weigh 25-30 / 30-35 / 10-15 / 10-15 / 10-15, with generative AI and agents the largest. Official specs: $165, 120 minutes, pass at 700, offered in Traditional Chinese, may include interactive components — and it is valid for only one year, though renewal is a free, open-book, unproctored online assessment.
AB-620 is the low-code branch of Microsoft's agent certification line — it tests agent flows, adaptive cards, computer use, MCP tools, A2A, and Fabric data agents in Copilot Studio, not Python. The three skill areas weigh 30-35 / 40-45 / 20-25, with integration the heaviest. Official specs: $165, 120 minutes, pass at 700, one-year validity, and 13 languages including Traditional Chinese. It is generally available, but the practice assessment is not out yet.
AI-500 is a rare thing among the major clouds — an expert-level certification dedicated to multi-agent systems, weighted 15-20 / 30-35 / 20-25 / 20-25, naming Agent Framework, LangGraph, Hugging Face Transformers, MCP servers on Azure Functions / Logic Apps / API Management, A2A, Key Vault, and the AI Red Teaming Agent. Two constraints come first: it requires AI-103 before you can take it, and neither the official learning paths nor a practice assessment is live yet. Microsoft removed the beta label from its pages at the end of September 2026.
CCDV-F is the engineer's exam among Anthropic's four certifications. The official blueprint has eight domains, and the heaviest — Applications and Integration at 33.1% — is led by Claude Application Design (8.6%) and Software Engineering Foundations (7.4%), meaning a third of the exam is API mechanics and ordinary software engineering. The counterintuitive part: Claude Code is only 3.1% and Eval only 2.6%, while the sibling Architect exam gives Claude Code 20%. Official specs: $125, 53 items, 120 minutes, pass at 720, valid 12 months, registration limited to Claude Partner Network organizations.
NCA-GENL is usually what a job posting means by 'NVIDIA Generative AI / LLM certification.' But the official blueprint diverges sharply from the name — Core Machine Learning and AI Knowledge 30%, Software Development 24%, Experimentation 22%, Data Analysis 14%, Trustworthy AI 10% — with LLM and RAG content scattered at bullet level rather than forming a domain, alongside spaCy, NumPy, Keras, and cross validation. The other thing to know first: the official recommended training was replaced in autumn 2026, and each area now lists just two items, free open-source deep learning course materials on GitHub and one LLM application development course. Official specs: $125, 1 hour, 50–60 items, two-year validity, English only, pass/fail with no score reported.
NCP-AAI is NVIDIA's professional-level agentic AI credential — $200, 120 minutes, 60–70 items, two-year validity. Of its ten areas the heaviest are Agent Architecture and Design at 15% and Agent Development at 15%; only NVIDIA Platform Implementation at 7% is explicitly tied to NVIDIA products, and the rest is general agentic engineering. Two things to know first: registration is not open (the Register button carries a 'Coming soon' label), and the web page and the PDF study guide differ on two weights (Deployment and Scaling 13% versus 5%, Run/Monitor/Maintain 5% versus 7%), so budget those as a range.
NCP-GENL is NVIDIA's professional-level LLM credential — $200, 120 minutes, 60–70 items. What separates it from every other GenAI exam is where the weight sits: Model Optimization 17% plus GPU Acceleration 14% is 31% on quantization, distillation, pruning, distributed parallelism, and CUDA profiling — not on calling APIs. Two things to know first: the Register button says Coming soon, so you cannot sit it yet; and the description text for Model Optimization and Fine-Tuning in the web page's weight table is misplaced, so prepare those two areas from the official PDF study guide.
NCA-GENM matches NCA-GENL on price, length, and level but not on emphasis: Experimentation rises to 25% (the heaviest), Core ML drops from 30% to 20%, and two new areas appear — Multimodal Data 15% and Performance Optimization 10%. The content covers U-Net, CLIP, diffusion models, multimodal loss functions, attention maps, and NVIDIA's Riva / NeMo / Triton / ACE SDKs. The official recommended training was replaced in autumn 2026: it is now one free open-source set of deep learning materials on GitHub plus one multimodal agent course, assigned differently per area, with the heaviest area, Experimentation, getting only the free materials. Official specs: $125, 1 hour, 50–60 items, two-year validity, English only.
AB-100 is the architect tier of Microsoft's agent line, weighted 25-30 / 25-30 / 40-45 with deployment and governance heaviest. Its outline runs on verbs like design, recommend, and propose — it tests judgment, not configuration. Three things to know first: it has a prerequisite — besides passing AB-100 you must hold one of the associate certifications on the official list (AI-103, AB-620, and AI-300 are all on it); it has a free practice assessment, the only one of Microsoft's three agent credentials that does; and one blurb on the exam page describes a compliance exam instead, so prepare from the study guide. Official specs: $165 in the US, 13 languages including Traditional Chinese, pass at 700, one-year validity.
CCAR-P is the most expensive and most senior of Anthropic's four exams ($175, 63 items, 120 minutes). Integration is the heaviest domain at 19%, but what really separates it from everything else in this series is the other two: Governance, Safety & Risk Management at 14% and Stakeholder Communication & Lifecycle Management at 14% — 28% combined on compliance, risk, discovery interviews, and delivery lifecycle rather than code. The guide names GDPR, HIPAA, and FedRAMP, and its Intended Audience explicitly excludes entry-level developers and anyone doing 'prompt writing without broader system design responsibility.'
CCAO-F is the cheapest of Anthropic's four exams ($99, 60 items, 120 minutes), aimed at people who work with Claude rather than build against it. The heaviest of its seven domains is Output Evaluation and Validation at 21% — spotting hallucinations, deciding when human review is required, and adapting outputs — with Governance, Risk, and Responsible Use at another 15%. Anthropic states plainly that it is not for developers building against APIs or designing agentic systems. One easily missed limitation: this credential does not count toward Claude Partner Network tier eligibility, while the other three do.
Microsoft AI-500, AB-620, AB-100, NVIDIA NCP-AAI, and Claude CCAR-F all test multi-agent architecture, and they overlap on seven things: orchestration topologies, A2A and MCP, per-agent identity boundaries, three-layer memory, observability and agent replay, human-in-the-loop, and guardrails at four intervention points. But four vendors use four vocabularies for the same ideas, and each exam has objectives that don't transfer — Microsoft names four context-window failure modes nobody else names, 7% of NVIDIA's is locked to NeMo and NIM, and Claude tests SDK-level details like stop_reason. One correction along the way: Google PMLE's wall-to-wall 'Agent Platform' is a Vertex AI rename, not a multi-agent domain.
Four certifications genuinely test RAG and retrieval evaluation: AWS AIF-C01 (chapters 2 and 3 total 52%, covering RAG, vector stores, and FM evaluation metrics), AWS AIP-C01 (11 of the 27 skill points in its 31% Domain 1 sit in vector storage and RAG), NVIDIA NCP-AAI (Knowledge Integration 10% plus Evaluation and Tuning 13%), and Microsoft AI-500 ('multi-agent RAG architecture' inside its 30–35% Develop area). Google PMLE contributes exactly one LLM-as-a-judge objective, and Claude CCDV-F — the developer certification people most readily assume covers RAG — has no retrieval objective across its eight domains, with Eval at just 2.6%. Includes a same-vendor foundational-vs-professional comparison, a four-vendor terminology map, non-transferable objectives, and a practice project.
Governance carries more weight on these exams than most engineers expect — CCAR-P is 14% governance plus 14% stakeholder work (28% non-technical), CCAO-F is 15%, AB-100's deploy-and-govern block is 40–45%, AIF-C01 is 14% responsible AI plus 14% security/compliance/governance. But across all fifteen official exam guides in this series, not one names the EU AI Act, the NIST AI RMF, or ISO/IEC 42001; the only regulations any of them names are CCAR-P's GDPR, HIPAA, and FedRAMP. So the use of this post isn't memorizing frameworks for an exam — it's using the three frameworks as a skeleton to file six certifications' scattered governance objectives. The three split cleanly: the EU AI Act is law (fully applicable 2026-08-02, high-risk duties pushed to 2027-12-02 by the AI Omnibus), the NIST AI RMF is voluntary (GOVERN/MAP/MEASURE/MANAGE, and 1.0 is currently being revised), and ISO/IEC 42001 is a certifiable management system standard whose clauses are paid, so this post covers only what is on ISO's public page.
Most people assume GenAI certifications are built around prompt writing. CCAO-F gives Prompting 14% while Output Evaluation gets 21%; CCDV-F gives Prompt and Context Engineering 11.0%. What actually gets tested is structured output, injection-resistant prompting, dynamic context injection, context compression and caching, prompt lifecycle governance, and proving a prompt change helped — closer to context engineering and software engineering than to writing craft. None of the ten asks you to write a prompt on the spot; they are all multiple choice, so explaining why beats having a feel for it. The single most useful line comes from CCAR-F: when business logic must be guaranteed, 'change the prompt first' is usually the wrong answer.
Google PMLE, AWS AIF-C01 and AIP-C01, Microsoft AI-103 and AI-500, and NVIDIA NCP-GENL all test how to make a GenAI application fast, cheap, and reliable — and they form a three-rung ladder: AIF-C01 asks whether you know cost scales with tokens, AIP-C01 and the two Microsoft exams ask whether you can instrument and control it, NCP-GENL asks whether you can change the model and the hardware. Three different altitudes. NVIDIA works at the kernel and quantization layer (Model Optimization 17% + GPU Acceleration 14% = 31%, the heaviest single cost/latency block in the whole series), AWS and Microsoft at the application layer (three caching tiers, token caps, chargeback), and Google at the MLOps layer (CPU/GPU/TPU evaluation, data vs model parallelism, scaling serving backends by throughput). The shared core is eight levers, but each lever becomes a different question at each altitude. This post deliberately carries no prices and no hardware specs — that is the part of this topic that rots fastest.
AIF-C01, MLA-C02, and AIP-C01 are not a difficulty ladder but three job-function slices: AIF tests AI business judgment, MLA tests whether you can put traditional ML, foundation models, and agentic workflows into production, and AIP tests whether you can integrate foundation models into a GenAI system. The MLA-C02 English beta is open for registration; general-availability dates and non-English versions remain unannounced.
Across Microsoft's four AI/agent certifications there are two hard prerequisites: AI-500 requires AI-103, and AB-100 requires any one associate on its list. The rest is positioning. Three forks decide it: whether you write Python (AI-103/AI-500 vs AB-620), whether you build or judge (AB-100 vs the rest), and whether you can actually start today — AI-500's four official learning paths currently 404, AB-620 has no practice test, AB-100 has a free one. For readers who prefer Chinese there is a fourth fork: AI-103, AB-620, and AB-100 offer Traditional Chinese, and only AI-500 is English only. All four cost $165 in the US ($83 in Taiwan), expire after one year, and renew free but only inside a six-month window.
NVIDIA's generative AI line has four exams: NCA-GENL and NCA-GENM ($125 each, associate), NCP-GENL and NCP-AAI ($200 each, professional). Three decision inputs no other vendor forces on you. One: both professional exams still show 'Coming soon' next to Register, so any near-term plan is down to the two associates. Two: the cost of the official recommended training differs sharply by level. In autumn 2026 the two associate exams switched to one free open-source set of materials plus one course, while the two professional exams still list five paid courses, $1,620 at list price for NCP-GENL. Three: the official web pages and PDFs differ in a few places — two NCP-AAI weights do not match, and two cells of NCP-GENL's web table carry misplaced text — so for those two exams follow the PDF or budget a range. Lock-in also varies sharply: NCP-AAI is 7% NVIDIA-specific, NCP-GENL is 31% GPU and model-compression work.
Before comparing exam objectives there is one fact that outranks all of them: registration for the Claude certifications is open only to organizations in the Claude Partner Network — individuals cannot sign up. For those who clear that gate, four things actually decide the answer. CCAO-F ($99) does not count toward partner tier eligibility while the other three do. Claude Code is 20% of CCAR-F but only 3.1% of CCDV-F — the architect exam tests the tool far more heavily than the developer exam. CCAR-P is 28% non-technical (governance 14% plus stakeholder communication 14%), which nothing else in this series is. And CCAO-F is the cheapest but only 14% Prompting; Output Evaluation at 21% is its real spine. All four are valid 12 months, with retakes at 14 / 30 / 90 days and 4 attempts per rolling 12 months.
AI-901 replaces AI-900, retired June 30, 2026. The certification is still called Azure AI Fundamentals, but the objectives are now two areas: concepts at 40–45% and implementing with Microsoft Foundry at 55–60%. Python is an explicit requirement, and the objectives include building a lightweight chat client with the Foundry SDK and creating a single agent. Official specs: $99 in the US, $50 in Taiwan, 45 minutes, pass at 700, 13 languages including Traditional Chinese, and fundamentals certifications never expire.
AI-200 (Azure AI Cloud Developer Associate) succeeds AZ-204, retired July 31, 2026. Its four areas weigh 20–25 / 25–30 / 20–25 / 20–25: containers, data services, messaging and serverless, security and monitoring. Nothing in the objectives covers model deployment, prompts, or agents; the closest it gets to AI is vector search on Cosmos DB, PostgreSQL pgvector, and Managed Redis. Official specs: $165 in the US, $83 in Taiwan, 120 minutes, pass at 700, 13 languages including Traditional Chinese, one-year validity, and no practice assessment yet.
AI-300 (Machine Learning Operations Engineer Associate) succeeds DP-100, retired June 1, 2026. The five areas weigh 15–20 / 25–30 / 20–25 / 10–15 / 10–15: the first two are classic MLOps on Azure Machine Learning at 40–50% combined, and the last three are GenAIOps on Microsoft Foundry at 40–55%. The objectives name MLflow, Bicep, Azure CLI, and GitHub Actions. Official specs: $165 in the US, $83 in Taiwan, 120 minutes, pass at 700, one-year validity, English only.
GH-300 is the official GitHub Copilot certification. After the August 7, 2026 revision the outline has six areas, the heaviest being 'Use GitHub Copilot features' at 25–30%, which covers the Copilot CLI, agent mode, MCP, sub-agent delegation, and organization-level policy, audit logs, and the REST API. Official specs: $99 in the US, $50 in Taiwan, 100 minutes, 60 scored questions, pass at 700, valid 2 years, five languages with no Chinese, and an official practice assessment. The official weight list has one extra line; prepare from the six real sections.
GH-600 is GitHub's agent certification (listed as Intermediate), covering how to operate, supervise, and govern AI agents inside a software development workflow. The six areas weigh 15–20 / 20–25 / 10–15 / 15–20 / 15–20 / 10–15, the heaviest being tools and environment (MCP, permissions, CI invocation, error handling). The most common verb in the objectives is configure (15 of 65), followed by identify and implement. Official specs: $165 in the US, $83 in Taiwan, 120 minutes, pass at 700, valid 2 years, English only, no official practice assessment, and under 6 hours of official self-paced material.
MLA-C02 replaced the English MLA-C01 on September 29, 2026. The four domains weigh 28 / 24 / 24 / 24, and each domain name now carries AI or FM: the official comparison lists 45 added skills covering vector databases, embeddings, RAG, selecting and fine-tuning foundation models, and deploying and monitoring agents, with 7 removed. It is currently an English-only beta (exam code ME1-C02, 170 minutes, 85 questions, $75). The standard version per the exam guide is 65 questions, pass at 720, valid 3 years, with Japanese, Korean, and Simplified Chinese arriving at general availability; there is no Traditional Chinese version.