Test-Time Compute: Finding the Right Answer Among More Attempts | Stanford CS329A Lecture 2
More candidates increase the chance of a correct solution; credible selection turns that chance into delivered success.
ai
1239 postsRobust Verification: Checking Steps Before Trusting an Answer | Stanford CS329A Lecture 3
A correct final answer does not validate its derivation; step feedback locates errors, but verifiers still need checking.
Tool and Code Feedback: Changing the Next Action | Stanford CS329A Lecture 4
Observations, tests, and principle-based critique supply different signals; identify the signal before choosing revision or training.
Multi-Step Planning: Decompose the Step That Fails | Stanford CS329A Lecture 5
Distinguish search, parallel execution, and training; subtask artifacts must fulfill the original task criterion.
AWS Machine Learning Engineer Associate (MLA-C02): 45 of Its 107 Skills Are New
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.
GitHub Copilot Certification (GH-300): Beyond Autocomplete to the CLI, Agent Mode, and Organization Policy
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.
GitHub Agentic AI Developer (GH-600): It Tests How You Run Coding Agents, Not How You Write Them
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.
Microsoft AI-200 (Azure AI Cloud Developer): AI Is in the Name, but It Tests the Backend That Keeps AI Apps Running
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.
Microsoft AI-300 (Machine Learning Operations Engineer): One Exam, Two Operations Disciplines, Split Evenly Between MLOps and GenAIOps
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.
Microsoft AI-901 (Azure AI Fundamentals): An Entry-Level Exam Where Over Half the Weight Is Building With Foundry
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.
ML/LLM System Design and Coding Interviews: Four Architectures, Three Python Problems, and a STAR Skeleton
Four system design questions that come up often (enterprise RAG, an LLM serving stack, a customer-support agent, online A/B testing with a data flywheel), three Python problems (dynamic batching, sampling, backoff retries plus a token bucket), and a STAR skeleton for behavioral rounds. Each design question has a speakable answer and a self-check list, and all code was actually run.
Audio, Video, and World Models: A Reading Map | CMU 10-423 Topic Guide
Audio representation, temporal relationships in video, and action-conditioned world transitions are different learning tasks. The earlier combined guide checked only audio at the end. This URL now serves as a reading map to one full guide each for Lectures 24–26; the Lecture 26 guide also covers the separate alignment deck.
CNNs and ViT: Turning an Image into Model Inputs | CMU 10-423 Lecture 5
Text models convert text into tokens; image models also need a computational representation of pixels.
Code Generation and Agents: A Reliable Test–Repair Loop | CMU 10-423 Lecture 23
Code Generation and Agents: A Reliable Test–Repair Loop | CMU 10-423 Lecture 23
Condition Injection: The Roles of Attention, DiT, and Q-Former | CMU 10-423 Topic Guide
Cross-attention reads another representation, DiT is a diffusion-backbone choice, and Q-Former uses learned queries to extract or bridge information. They can coexist rather than being three competing products. The Lecture 14 lesson handles Prompt-to-Prompt and classifier-free guidance in detail.
Diffusion Models: Why Adding Noise Creates a Training Task | CMU 10-423 Lecture 7
A GAN learns from a discriminator.
Distributed Training and Efficient Inference: Locate the Cost | CMU 10-423 Topic Guide
A model that does not fit on one GPU has a different problem from a model that fits but decodes slowly. Distributed strategies partition data, parameters, and states. FlashAttention improves attention memory traffic; KV caching avoids recomputing historical keys and values during decoding.
Exams, Papers, and Projects: Three Forms of Learning Evidence | CMU 10-423 Topic Guide
An exam tests reasoning under a fixed question. A paper report tests your judgment of someone else’s evidence. A project tests your ability to produce reproducible evidence. These are distinct learning tasks, now taught separately while this URL remains an assessment map.
GANs: What the Generator and Discriminator Learn | CMU 10-423 Lecture 6
A classifier asks which class an image belongs to.
A Generative AI Learning Path: Reading the 35 Core Guides | CMU 10-423 Course Overview
This series uses the public Spring 2026 materials for CMU 10-423/623/723 Generative AI.