AI & ML Proficiency Quiz
For Data Scientists
A rigorous 20-question assessment covering classical ML, deep learning, experiment design, MLOps, and modern generative AI — designed to benchmark real data science competency in 2025.
Ready to test your knowledge?
20 questions · 20–25 minutes · Intermediate to Advanced
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👤 Who is this for?
Designed for data scientists, ML engineers, and analytics engineers across all experience levels. Whether you're a junior data scientist preparing for interviews or a senior practitioner benchmarking your knowledge of modern ML and generative AI, this quiz covers the full spectrum.
💡 Why AI literacy matters
Data science is evolving fast. The skills required in 2025 span classical ML, causal inference, MLOps, and now LLM evaluation. Gaps in fundamentals — like data leakage, proper experiment design, or model monitoring — lead to shipped models that fail silently in production.
Topics Covered in This Assessment
Frequently Asked Questions
What AI and ML topics should data scientists know in 2025?
Data scientists in 2025 need strong foundations in model evaluation and selection, experiment design (A/B testing, causal inference), feature engineering, bias and fairness, MLOps and model monitoring, and practical knowledge of LLMs and generative AI — including how to evaluate and fine-tune them.
Is this quiz useful for machine learning interview preparation?
Yes. The quiz covers core concepts that appear in data science and ML engineer interviews: bias-variance tradeoff, cross-validation, regularization, class imbalance, experiment design, and LLM evaluation. The written questions mirror take-home or system design prompts.
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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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