The AWS Certified AI Practitioner exam is 65 questions in 90 minutes, and you need 700 out of 1000 to pass. Set how you’re scoring on practice questions in each of its 5 domains and get an estimated scaled score, weighted the way AWS weights AIF-C01. Free, no signup.
How are you scoring on practice questions, per domain?
Estimated scaled score
700 / 1000
Pass mark: 700
+0 points vs pass
Borderline — could go either way
You're inside the noise band around the pass mark, and exam-day pressure usually costs a few points. Don't reschedule yet: the lever list below shows where a week of targeted drilling buys the most points.
These sliders are your guess about yourself. The free 10-question diagnostic measures it — per domain, against the same blueprint weights, no signup. Then drill the gaps with the free AI Practitioner practice questions and schedule the rest with the study plan generator.
Measure it with the free diagnostic| Domain | Weight | ≈ Scored Qs | Pts per +10% |
|---|---|---|---|
| Applications of Foundation Models | 28% | 14 | 28 |
| Fundamentals of Generative AI | 24% | 12 | 24 |
| Fundamentals of AI and ML | 20% | 10 | 20 |
| Guidelines for Responsible AI | 14% | 7 | 14 |
| Security, Compliance & Governance for AI | 14% | 7 | 14 |
On AIF-C01, Applications of Foundation Models is worth the most: 28% of the score, so lifting your accuracy there by 10% adds about 28 scaled points, while the same effort in Security, Compliance & Governance for AI (14%) adds about 14. That's 2.0× the return, so if two domains are equally weak, fix Applications of Foundation Models first. The pass mark of 700 works out to roughly 70% blueprint-weighted accuracy on this estimate.
700 out of 1000 on AWS's scaled score. The exam has 65 questions in 90 minutes; 15 are unscored pretest items you can't identify, so 50 count. Scoring is compensatory: there is no per-domain minimum, only the overall score.
Applications of Foundation Models, at 28% of the exam (about 14 scored questions). Each 10% of accuracy there is worth roughly 28 scaled points, against 14 in Security, Compliance & Governance for AI (14%).
It splits AI knowledge into fundamentals of AI and ML (20%), generative AI (24%) and applications of foundation models (28%), then adds two 14% domains: responsible AI, and security, compliance and governance for AI. Those two together are worth 28%, as much as the biggest domain, so skipping the governance material costs as much as skipping foundation models.