AI Engineering Proficiency Quiz
For Software Engineers
Test your technical depth on AI/ML concepts — from transformer architecture and embeddings to RAG pipelines, inference optimization, security, and production system design. 20 questions, AI-evaluated.
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20 questions · 20–25 minutes · Advanced
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👤 Who is this for?
Built for software engineers, backend engineers, ML engineers, and AI/LLM application developers who build or integrate AI systems. Ideal for engineers preparing for AI-focused interviews, evaluating their own knowledge gaps, or onboarding onto AI product teams.
💡 Why AI literacy matters
LLM engineering is now a core competency for software engineers across the industry. Understanding inference optimization, security boundaries, RAG architecture, and evaluation methodology separates engineers who can ship reliable AI systems from those who ship fragile demos.
Topics Covered in This Assessment
Frequently Asked Questions
What AI/ML skills should software engineers have in 2025?
In 2025, software engineers should understand transformer architecture, tokenization, embeddings, vector databases, RAG pipeline design, fine-tuning techniques (LoRA, QLoRA), inference optimization (quantization, KV cache, speculative decoding), prompt injection security, and how to design observable, scalable AI systems.
Is this a good test for LLM engineering interview prep?
Yes. The quiz covers the exact concepts that come up in AI/ML engineering interviews: architecture fundamentals, inference optimization, system design tradeoffs, RAG, fine-tuning, and security. The open-ended questions mirror real system design interview prompts.
Do I need a math background to take this quiz?
No heavy math required. The quiz tests conceptual and applied engineering knowledge — understanding what techniques do and when to use them — rather than deriving equations.
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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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