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208 published articles
To move a Microsoft Power Platform extension across environments repeatably, capture its solution components in source control, establish dependencies and target configuration, produce a versioned release artifact, validate it in test,…
Designing security and Dataverse data boundaries starts with four decisions: identify the principal that actually performs each operation, define which data that principal may access, choose a table model that can enforce the required…
Choose Power Platform extension architectures by separating three decisions: what must be enforced when data changes, what can finish later, and how the user should interact.
AB-400 assesses developers who extend Microsoft Power Platform through custom user experiences, server logic, automation, integrations and agentic AI. It is associated with Microsoft Certified: Power Platform Developer Associate.
Build an evidence-based study sequence by mapping the current AI-300 skills to prerequisites, working from foundations toward operational decisions, and retaining an artifact for each outcome.
Use the AI-300 study guide to determine the skills in scope, the certification page for credential positioning and current administrative navigation, and product documentation for how a task works and which limitations apply.
Use this readiness checklist after learning the foundations: mark an item when you can explain the distinction, justify a choice and identify supporting evidence from your own work or a clearly labeled design rehearsal.
Promote a fine-tuned model only when its exact artifact is traceable to the base model, reviewed data, training method and run; independent held-out evaluation supports the intended use; and the target serving path supports that artifact…
Turn synthetic examples into training data through a qualification process: verify source permission and privacy, define the behavior and generation constraints, inspect a small pilot, review and filter the output, keep related cases…
Choose supervised fine-tuning (SFT) when you can demonstrate desired responses, direct preference optimization (DPO) when you can compare preferred and rejected responses to the same input, and reinforcement fine-tuning (RFT) when you can…
Test a retrieval change by running baseline and candidate against the same frozen questions and source snapshot, then measuring retrieved evidence and generated answers separately.
Change an embedding model when a controlled comparison shows that its representation of your queries and content is a material cause of retrieval failures.
