Google Cloud Generative AI Leader: Generative AI Strategy
A generative AI strategy is not a list of models to buy. It is a business plan for choosing valuable use cases, matching…
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Microsoft DP-600: Semantic Model Design for Power BI
A Power BI semantic model is the layer that turns stored data into consistent business meaning. It defines relationships, measures, hierarchies, security, formatting,…
Read articleMicrosoft DP-600: Row-Level Security in Fabric and Power BI
Row-level security controls which records a user can see without requiring a separate report or dataset for every audience. In Microsoft Fabric and…
Read articleMicrosoft DP-600: OneLake Security and Data Governance
OneLake gives Microsoft Fabric a unified storage foundation, but unified storage does not mean unrestricted storage. As more lakehouses, warehouses, semantic models, and…
Read articleMicrosoft DP-600: Monitoring and Optimizing Fabric Capacity
Microsoft Fabric capacity is the shared compute foundation behind workloads such as data engineering, warehouses, semantic models, data science, and reporting. That shared…
Read articleMicrosoft DP-600: Fabric Lakehouse Architecture
A Microsoft Fabric lakehouse is more than a place to put Parquet files. It is an analytical architecture that lets engineering, SQL analytics,…
Read articleMicrosoft DP-600: Medallion Architecture in Microsoft Fabric
Medallion architecture organizes analytical data into progressively refined layers, usually called bronze, silver, and gold. The labels are simple, but the value comes…
Read articleMicrosoft DP-600: Fabric Data Pipelines and Dataflows Gen2
Microsoft Fabric Data Factory offers several ways to move and transform data, but two components are frequently confused: pipelines and Dataflow Gen2. They…
Read articleMicrosoft DP-600: Direct Lake vs Import vs DirectQuery
Choosing a Power BI semantic-model storage mode is an architecture decision, not a tuning checkbox. Import, DirectQuery, and Direct Lake determine where query…
Read articleMicrosoft AB-100: Securing Tool Access for Agentic AI
An AI agent becomes materially more powerful when it can call tools. Search, databases, email, ticketing systems, code execution, APIs, and Model Context…
Read articleMicrosoft AB-100: Observability for Multi-Agent Workflows
A multi-agent workflow can fail while still producing a plausible final answer. One agent may retrieve the wrong source, another may choose an…
Read articleMicrosoft 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 articleMicrosoft 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…
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