Microsoft AB-100: Human-in-the-Loop Governance for AI Agents
Human-in-the-loop governance is often described as adding an approval button to an AI workflow. That is too narrow. The real design problem is…
Read articleVENDOR / EXAMTOPICS EDITORIAL
Microsoft.
Editorial material about Microsoft and its technologies, assessments and credentials.
PUBLISHED COVERAGECurated by subject
Microsoft AB-100: Grounding Enterprise Agents with Trusted Data
Enterprise agents become useful when they can work with information that is specific to the organization: policies, product catalogs, contracts, customer records, knowledge…
Read articleMicrosoft AB-100: Evaluating AI Agents Pre-Production
An AI agent can produce impressive demonstrations and still be unsafe to release. The difference is that production exposes the agent to messy…
Read articleMicrosoft AB-100: Enterprise AI Agents with Microsoft Foundry
Building an enterprise AI agent is less about finding a clever prompt and more about assembling a governed runtime around a model. The…
Read articleMicrosoft AB-100: AI Agent Orchestration for Business Workflows
Agentic AI becomes genuinely useful when it can participate in a business process rather than merely answer a question. That usually means coordinating…
Read articleMicrosoft AB-100: AI Agent Governance in Regulated Industries
AI agents create a governance problem that is different from ordinary chatbots. A chatbot primarily produces information. An agent can maintain state, choose…
Read articleMicrosoft AI-300: Responsible AI in Production
Responsible AI controls for production systems Responsible AI becomes concrete only when a system is exposed to real users, real data, and real…
Read articleMicrosoft AI-300: Prompt Versioning and Release Management
A prompt is production logic when changing it can change what an AI application says or does. That makes prompt management an engineering…
Read articleMicrosoft AI-300: Model Evaluation & Monitoring in Microsoft Foundry
Evaluate Microsoft Foundry models and agents in production Putting a generative AI application into production creates two separate questions. The first is operational:…
Read articleMicrosoft AI-300: Managing Model and Data Drift
A machine-learning model can remain technically available while becoming less useful. The endpoint responds, latency stays normal, and error rates look healthy, yet…
Read articleMicrosoft AI-300: Generative AI MLOps in Azure
Generative AI changes the operating model of machine learning. A conventional model release can often be described in terms of training data, code,…
Read articleMicrosoft DP-700: Spark Notebooks in Microsoft Fabric
Notebooks are one of the most flexible ways to work with Apache Spark in Microsoft Fabric. They let a data engineer explore a…
Read articleMicrosoft DP-700: Incremental Data Loads in Fabric
Incremental loading is one of the most important techniques for keeping analytical data current without moving the entire source dataset on every run.…
Read articleMicrosoft DP-700: Fabric Lakehouse Data Quality
A lakehouse becomes valuable only when people can trust the data that moves through it. Microsoft Fabric gives teams a unified storage and…
Read articleMicrosoft DP-700: Reliable Fabric Data Pipelines
A data pipeline is reliable when a team can trust it to move and transform data repeatedly without turning every transient failure, schema…
Read article