Describe Artificial Intelligence Workloads and Considerations
The workload families
AI-900 expects you to match a scenario to a workload family: machine learning (predicting a value or category from data), computer vision (interpreting images and video), natural language processing (understanding, translating and producing text and speech), document intelligence (pulling structured fields out of forms, invoices and receipts), knowledge mining (building a searchable index over unstructured content), and generative AI (creating new text, images or code).
The six responsible AI principles
- Fairness - similar outcomes for people in similar situations, regardless of group.
- Reliability and safety - the system behaves predictably and fails safely.
- Privacy and security - personal data is protected and used with consent.
- Inclusiveness - everyone can use the system, including people with disabilities.
- Transparency - people understand how the system works and what data it uses.
- Accountability - humans remain responsible for the system's decisions and can be asked to answer for them.
Traps that show up repeatedly
Transparency versus accountability is the classic swap: explaining a model is transparency; signing off on its output is accountability. Fairness versus inclusiveness is the second: fairness is about comparable performance across groups, inclusiveness is about accessibility. Also remember that Microsoft's guidance applies to the application you build, not only to the Azure service.
Worked example
A bank's loan model rejects a higher proportion of applicants in one age band: that is a fairness issue. Leadership asks how the model scores applicants: that is transparency. Requiring a human officer to review every rejection is the accountability control.
Rule of thumb: if the question is about who is answerable, answer accountability; if it is about who can see how it works, answer transparency.