WebLizard Labs · Reviewed September 27, 2026

AB-100 skills self-assessment checklist

English objectives: October 14, 2026

These resources target the English AB-100 objectives effective October 14, 2026. This is an incoming version at the September 27, 2026 source review. If you test before October 14, use the objectives that apply on your exam date. Localized exams can update later; confirm your language and exam date with Microsoft.

This checklist groups the ten skill areas in the target guide; it is not the complete list of objective bullets. Revisit every bullet in the official guide. Explain = can justify a decision; Practice = have tested or worked a scenario; Refresh = have rechecked current documentation. Mark each independently only when you have evidence; an unchecked box is an open gap, not a failing score.

Entries stay only in this open page and are not saved or submitted. Print before closing, or use the CSV in a spreadsheet. Leave unchecked items as gaps; record the next action and review date.

Plan AI-powered business solutions (25–30%)

A01. Analyze requirements for AI-powered business solutions

Explain whether an agent adds value to a defined business task. Identify grounding-data quality gaps.

Practice: Inspect a small, non-sensitive dataset; record missing, stale, or inaccessible evidence.

Official skill-area objectives

A02. Design overall AI strategy for business solutions

Defend a build, extend, or prebuilt approach across the Microsoft platforms in scope.

Practice: Sketch a multi-agent boundary and write a prompt-library rule tied to a business constraint.

Official skill-area objectives

A03. Evaluate the costs and benefits of an AI-powered business solution

Explain benefit measures, operating cost, and the risk of optimistic ROI assumptions.

Practice: Compare two options using stated workload, cost, maintenance, and benefit assumptions.

Official skill-area objectives

Design AI-powered business solutions (25–30%)

A04. Design AI and agents for business solutions

Explain how agent type, grounding, fallback, and orchestration fit the business process.

Practice: Prototype one safe interaction and capture expected behavior, fallback, and acceptance criteria.

Official skill-area objectives

A05. Design extensibility of AI solutions

Explain what extending an agent changes about tools, permissions, knowledge, and trust.

Practice: Draw a tool invocation boundary; identify caller identity, authorization, and failure handling.

Official skill-area objectives

A06. Orchestrate configuration for prebuilt agents and apps

Explain why a prebuilt Dynamics 365 or Microsoft 365 capability fits a business scenario.

Practice: Map the required configuration and knowledge sources; list tenant or licensing facts to verify.

Official skill-area objectives

Deploy AI-powered business solutions (40–45%)

A07. Analyze, monitor, and tune AI-powered business solutions

Explain how telemetry and user feedback expose behavior or performance problems.

Practice: Design a monitoring note with a useful metric, alert condition, accountable owner, and tuning action.

Official skill-area objectives

A08. Manage the testing of AI-powered business solutions

Explain success and failure criteria for prompts, agents, models, and end-to-end workflows.

Practice: Write normal, denied-access, missing-evidence, and unsafe-output test cases.

Official skill-area objectives

A09. Design the ALM process for AI-powered business solutions

Explain how agent, model, connector, and grounding-data changes move through environments.

Practice: Draft a versioned change record, test gate, deployment check, and rollback decision.

Official skill-area objectives

A10. Design responsible AI, security, governance, risk management, and compliance

Explain access control, residency, audit trails, responsible AI, and prompt-manipulation risk.

Practice: Threat-model one data flow; document a preventive control, evidence, residual risk, and owner.

Official skill-area objectives