Overfitting occurs when a model memorizes training data including noise, reducing generalization. Regularization and cross-validation help prevent it.

NoSQL databases prioritize flexible schemas and horizontal scaling. They may use eventual consistency (BASE model) rather than strict ACID.

Sharding partitions data based on a key (e.g., user ID) across servers. This horizontal scaling improves performance and handles large datasets efficiently.

Activation functions (ReLU, Sigmoid, Tanh) add non-linearity, allowing neural networks to learn non-linear relationships. Without them, networks become linear classifiers.

Microservices decompose applications into loosely-coupled, independently deployable services. This enables scalability, flexibility, and technology diversity.

Connection pooling maintains a pool of reusable connections, reducing the cost of opening/closing connections repeatedly. This significantly improves application performance.

Zero-day vulnerabilities are previously unknown exploits with no patch. They're extremely dangerous because users have no defense until vendors develop and release fixes.

Insertion Sort has O(n) best-case complexity for nearly sorted data. Other algorithms don't exploit this, maintaining O(n log n) regardless of initial order.

BASE is an alternative to ACID, prioritizing availability and partition tolerance. It accepts temporary inconsistencies that resolve eventually (eventual consistency).

Least privilege minimizes security risk by restricting access. Users get only permissions needed for their role, reducing damage from compromised accounts.