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When you need the largest or smallest element repeatedly but do not need everything sorted, a heap gives you O(log n) per operation instead of a full O(n log n) sort each time. Top-k is the canonical use; the two-heap trick for a streaming median is the one that separates candidates. Remember that a size-k heap solves top-k in O(n log k), which is the answer interviewers are usually fishing for.
"Top/smallest k", a running median, or repeatedly needing the current minimum.
10 problems. Each one runs in an in-browser editor against real test cases, with hints and a worked solution.
The patterns cheat sheet covers all of these in one page - what each one looks like in a problem statement, and the template that follows.