The CAP theorem says that in a distributed system you can’t fully guarantee Consistency, Availability and Partition tolerance at the same time. When a network partition happens, the system must choose between staying consistent or staying available.
Advanced answer
Deep dive
Expanding on the short answer — what usually matters in practice:
Explain the "why", not just the "what" (intuition + consequences).
Trade-offs: what you gain/lose (time, memory, complexity, risk).
Edge cases: empty inputs, large inputs, invalid inputs, concurrency.
Examples
A tiny example (an explanation template):
// Example: discuss trade-offs for "what-is-the-cap-theorem?"
function explain() {
// Start from the core idea:
// In a distributed system, you can only have 2 of 3: Consistency, Availability, Partition To
}
Common pitfalls
Too generic: no concrete trade-offs or examples.
Mixing average-case and worst-case (e.g., complexity).