Generative AI and agentic solutions are central to AI-103. The candidate needs to understand more than prompting. A useful solution usually needs grounding data, evaluation, orchestration, tool use, monitoring, and controls that reduce unsafe or low-quality outputs.
Microsoft Foundry is the editorial center of this topic on QZ9 because it connects models, agents, evaluation, and application lifecycle work. Study how a solution selects a model, adds retrieval or grounding data, manages prompts, defines agent actions, and checks quality after deployment.
What to know for agent questions
An agent question may ask what the system should do next: retrieve context, call a tool, validate output, apply content safety, monitor usage, or improve the data source. The correct answer depends on the stage of the workflow. That is why the AI-103 exam objectives should be reviewed together with scenario practice.
Use the AI-103 Practice Test to check whether you can separate model behavior, application orchestration, and operational monitoring. If the whole exam scope feels wide, return to the AI-103 study guide and study one workload at a time.
Verified against the official vendor reference: learn.microsoft.com.