AI Proficiency Quiz
For Product Managers
Test your understanding of AI concepts, strategy, ethics, and practical application in product management. 20 questions covering everything from LLMs to AI roadmapping, evaluated instantly by AI.
Ready to test your knowledge?
20 questions · 15–20 minutes · Intermediate
You'll need your email address to receive results. Multiple choice answers can be changed before submitting. Written answers are evaluated by AI.
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👤 Who is this for?
This assessment is designed for product managers, associate PMs, senior PMs, and product leaders who work on or alongside AI-powered products. Whether you're a PM at a startup shipping your first LLM feature or a VP of Product setting AI strategy at scale, this quiz will benchmark your applied AI knowledge.
💡 Why AI literacy matters
AI literacy is no longer optional for product managers. Teams that ship AI features without understanding model limitations, evaluation frameworks, and responsible AI principles risk product failures, user harm, and reputational damage. This assessment helps you identify gaps before they become expensive.
Topics Covered in This Assessment
Frequently Asked Questions
What AI skills should a product manager have in 2025?
Product managers in 2025 should understand LLM capabilities and limitations (hallucinations, context windows), prompt engineering basics, RAG vs fine-tuning tradeoffs, AI evaluation frameworks, responsible AI principles, and how to define success metrics for AI features. Deep ML expertise is not required, but strategic and risk literacy is essential.
Is this AI quiz free for product managers?
Yes, completely free. No registration, no credit card, no usage limits. Take it as many times as you like.
How is this assessment scored?
Multiple choice and true/false questions are scored automatically. Open-ended written questions are evaluated by AI using a detailed rubric, then combined with your MCQ score for a final result.
What level of AI knowledge does this test assume?
The quiz is designed for PMs with some exposure to AI products — not total beginners, but also not ML engineers. It focuses on strategic and applied knowledge rather than math or code.
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Simone Banks created an AI app to parse payment info from invoices.
Marcus Chen built an agent to summarize meeting notes from documents.
Elena Rodriguez made an app to extract customer feedback from Excel files.
James Mitchell created an agent to analyze competitor pricing from web links.
Pritika Rajan built an AI app to generate weekly reports from sales data.
Alexis Turner made an agent to extract key insights from research papers.
Sofia Martinez created an app to categorize support tickets automatically.
David Park Chung built an agent to convert PDFs into structured databases.
Rachel M Foster made an AI app to track project milestones from documents.
Amanda Zhang created an agent to analyze sentiment from customer reviews.
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