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.
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.
Vertex AI is more than the Gemini API: it puts access to 200+ models, training, evaluation, deployment, and governance under one Google Cloud control plane. Since April 2026, its products and roadmap have moved into Gemini Enterprise Agent Platform, while the Vertex AI API, documentation paths, and many resource names remain in active use.
The core of ML System Design interviews isn't choosing the model — it's how to turn a business objective into a system that's deployable, monitorable, and iterable. Interviewers want to see if you can: translate business goals into ML objectives, design data pipelines and feature stores, choose reasonable serving strategies, and plan monitoring and A/B testing.
MLOps interviews test whether you have experience pushing models to production. Key topics: ML pipeline CI/CD (how it differs from software CI/CD), model registry and version management, A/B testing design and pitfalls, inference scaling strategies (horizontal scaling, model compression, caching), and production monitoring and alerting design.
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.
Andrew Ng walks a face-recognition door system through the entire project lifecycle, and the whole lecture has one thesis: speed. He gives teams a two-day deadline, on the reasoning that 'time spent preparing data should be commensurate with the time it takes to train the model once.' It closes on a line: my job is to build something that actually works, and that is not the same as building something that works on the test set.