Audit your comprehension of fundamental parameter mechanics, memory hierarchies, compute tradeoffs, and failure modes before progressing to neural networks and deep learning.
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// SCENARIO_01 • LIFECYCLE & COMPUTE
Why does model inference typically require significantly less memory (VRAM) and compute than training the same model?
// SCENARIO_02 • PARAMETERS VS HYPERPARAMETERS
Which of the following is an internal learned parameter, rather than an engineer-configured hyperparameter?
// SCENARIO_03 • MODEL ARCHITECTURE
What fundamental mathematical limitation arises if a linear neuron lacks a bias term (i.e. z = w * x instead of z = w * x + b)?
// SCENARIO_04 • INFERENCE MECHANICS
What happens mathematically when you set the sampling temperature to 0.0 in an autoregressive Large Language Model?
// SCENARIO_05 • GENERALIZATION & RELIABILITY
What is the root cause of model "overfitting" during empirical training?
// SCENARIO_06 • AI ENGINEERING SYSTEMS
What is the primary difference between a classical Software 1.0 architecture and a Software 2.0 (AI/ML) architecture?
// SCENARIO_07 • ENGINEERING ROLES
What is the core distinction between an AI Researcher and an AI Engineer?
// SCENARIO_08 • TOKENIZATION & INPUTS
What is a "token" in modern Large Language Models?
// SCENARIO_09 • PRODUCTION FAILURE MODES
What is "Distribution Shift" (or Data Drift) in production AI systems?
// SCENARIO_10 • ARCHITECTURE TRADEOFFS
In which scenario is an on-premise Local AI deployment (e.g. Llama 3 via vLLM) strictly preferable over a proprietary Cloud AI API (e.g. OpenAI/Anthropic)?