AIF I Study Plan: Build Dependable Product-Team AI Habits
Plan AIF I review around the required Scrum.org course, purposeful prompting, output evaluation, human accountability and varied original practice.
Start with the required course
AIF I is AI Fluency for Product Team Members I. Scrum.org requires the associated self-paced course, whose completion provides an assessment passcode. Confirm current assessment and course rules with the issuer. Independent PM Mastery practice is a supplement and provides no course completion, passcode or certification.
The published assessment has 40 multiple-choice questions in 60 minutes, an 85% passing score and English-language delivery. Exact native answer proportions are not established. Use the schedule below as a personal review structure, not an official duration or guarantee of readiness.
| Review block | Work to complete |
|---|---|
| Foundations | Identify suitable AI tasks, limits and observable failures in familiar product work |
| Prompting and iteration | Give useful context, diagnose an output shortfall and revise the direction deliberately |
| Quality and confidence | Verify a specific claim, apply output criteria and distinguish evidence from fluency |
| Accountability | Identify human ownership, relevant data rules and the review needed before use |
| Mixed practice | Complete the free 40 with a 60-minute practice allowance, then investigate every uncertainty |
Review the deciding facts
Answer before reading the explanation. State what makes the supported answer fit the case and why a nearby alternative does not. Track whether a miss came from weak direction, an unsupported inference, a source mismatch, an unstated policy or mistaken authority.
Revisit guessed correct answers. After studying a distinction, use a changed example or different app question to test transfer. Faster drafting and greater AI usage do not by themselves demonstrate improved skill or product value.
Keep the scope foundational
Use Level 1 Journey Map habits across discovery, delivery and support. Advanced architecture, model training and another provider’s AI taxonomy are not needed for these concise product-team judgments. Private course template labels and exact readiness rubrics should not be inferred from public descriptions.
Maintain useful unassisted skills and proportionate review. Progress can justify better task choices and more effective iteration without removing human ownership or local governance. Review practice-score limits ; reaching 85% in a fixed set does not validate an official-pass prediction.