Generating a correct candidate and recognizing it are different capabilities. This series follows the Autumn 2025 syllabus through a course overview and seventeen session guides covering all seventeen content sessions; five guest sessions have no public material and use public material from the speakers on similar topics instead; video Parts 7–9 map to sessions 8, 17 and 20.
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.
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.
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.
The 2026 consensus for agent-built slide decks: outline-first, separate content from construction, then render to images and let a fresh-eyes subagent do visual QA. Anthropic's and OpenAI's official slides skills both converged on PptxGenJS plus a visual verification loop, and the research line (PPTAgent → PreGenie → DeepPresenter) points the same way. But two later corrections matter: PresentBench shows the widely cited PPTEval scores too generously, and SeaSlides argues the model should not write free-form HTML/SVG at all.
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.
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.
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.
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.
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.
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.
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.
`batch_runner.py` runs thousands of prompts in parallel into ShareGPT-format tool-calling trajectories, lets each prompt name its own container image, and resumes by matching prompt content rather than index. Two quality filters run before you see the data: samples with zero reasoning are discarded, and entries calling hallucinated tool names are dropped at merge time. This is why a research lab builds a personal agent — the agent is the data pipeline.
One gateway process fronts 30-plus chat platforms and denies every user not on an allowlist or paired by DM. The scheduler adds two unusual guards: pre-dispatch validation marks a misconfigured job `blocked_config` without making a single LLM call, and the model drift guard makes unpinned jobs fail closed when the global model changes — protecting you from an hourly job quietly following you onto a paid model.
Hermes install paths come in three support tiers: macOS (Apple Silicon), Windows 10/11, Linux/WSL2, and Docker are Tier 1; Termux and Nix are Tier 2; pip, brew, AUR, and Intel Macs are explicitly unsupported — fixes for those won't be merged. On the upgrade side, `hermes update` snapshots state first, then compiles nine critical files after the pull and hard-resets the checkout if any fail to parse.
Hermes Agent is Nous Research's MIT-licensed agent framework, built around a learning loop: it writes its own skills, curates its memory, and searches past sessions with FTS5. It ships `hermes claw migrate` to move you off OpenClaw — but OpenClaw was not replaced, and both projects are still moving. This is the series opener: what it is, how it differs, and when not to pick it.
Hermes memory has hard caps: 2,200 characters for MEMORY.md and 1,375 for USER.md, and an over-limit write returns an error instead of auto-compacting, forcing the agent to make room itself. Skills are maintained by a curator that runs every 7 days after 2 hours of idle, marks skills stale at 30 days and archives at 90 — but never deletes. The switches actually worth flipping are `memory.write_approval` and `skills.write_approval`, which stage the background self-improvement writes for review.