How do you find the cheapest flight with at most K stops?

Question presented to candidate: "There are n cities and a list of one-way flights, each given as [from, to, price]. Given a source, a destination and a number K, find the cheapest price from source to destination with at most K stops, or return -1 if there is no such route. Please code it in Python."

What a strong answer should cover:

• Model it as a directed weighted graph and translate "at most K stops" into "at most K + 1 flights".
• Explain why plain Dijkstra is wrong here: a cheaper arrival with more flights can be a dead end, so the state must include the flight count.
• Bounded Bellman-Ford: K + 1 rounds of relaxing every flight, reading last round's prices and writing a copy (cost[:]), in O((K + 1) · E) time and O(n) space.
• The alternative Dijkstra over (cost, flights, city) states with heapq, and why it can skip a pop only when an earlier pop used no more flights.
• Python details: float("inf") as "unreachable" (and converting it back to -1), cost[:] or list(cost) for a shallow copy, and heapq being a min-heap over tuples.
• Edge cases: source equals destination, unreachable destination, K = 0.

Clarifying questions expected:

• Does K stops mean K intermediate cities, so K + 1 flights?
• Are prices always positive, and what should be returned when there is no route?

Code / implementation expected: Yes — find_cheapest_price(n, flights, src, dst, k) in Python 3, with bounded Bellman-Ford (or a heap-based state search).