Title: Quiz 1 — AI Infrastructure Foundations
Course: AIINFRA 100: AI Systems & Development Environments > Section: Week 01 — Introduction to AI Infrastructure

Q: AI infrastructure is best described as
- a single AI model
- the hardware, software, data systems, and practices that build/run/serve AI
- only GPUs
- a programming language
A: the hardware, software, data systems, and practices that build/run/serve AI

Q: Which is a cross-cutting concern that applies to every layer?
- CUDA
- PyTorch
- security & observability
- a GPU
A: security & observability

Q: A prototype that “works on my machine” is automatically production-ready.
- True
- False
A: False

Q: The two workload types that drive most infrastructure decisions are
- frontend & backend
- training & inference
- upload & download
- CPU & RAM
A: training & inference

Q: Which layer holds GPU drivers, CUDA, frameworks, and containers?
- Compute
- Software
- Data
- Serving
A: Software

Q: Name one thing that must change to move an AI demo from prototype to production.
Feedback: Sample/expected answer: reliability/monitoring, scaling to many users, security, cost control (any reasonable)

Q: In the AI system lifecycle, monitoring happens
- never
- only before training
- after deployment, continuously
- only if it breaks
A: after deployment, continuously

Q: Inference is the stage where a trained model is used to make predictions.
- True
- False
A: True