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This exam was created by a large language model. Please use it with caution, as inaccuracies may be present. Always verify critical information.

1.

Clearly define the challenge of finding the top K elements (where K=10) from a continuous stream of 'like' events without storing all incoming data.

2.

Explain why a Min-Heap is the appropriate data structure for finding the largest K elements from a stream, specifically for the 'Top 10 most liked posts' scenario, rather than a Max-Heap.

3.

Describe the data structure of the Min-Heap. What are its fundamental properties, and how will it store elements for tracking 'Top 10 most liked posts'? Specify the structure of each element (e.g., (value, identifier)).

4.

Detail the step-by-step algorithm for handling each incoming 'like' event using a Min-Heap of size K=10. Explain the process for:

Initialization: How the heap is populated when it has fewer than K elements.
Processing New Events (Heap Full): What happens when a new 'like' event arrives and the heap already contains K elements. Explain the comparison with the heap's root (minimum element) and the actions taken (insertion, extraction).
Updating Existing Post Likes: How to handle subsequent 'like' events for posts already tracked in the heap or posts that were previously outside the top K but now qualify.

5.

Discuss the time complexity for processing each 'like' event and the space complexity of this Min-Heap approach (with K=10). Compare its efficiency with naive approaches (e.g., storing all posts and sorting).

6.

Summarize the key advantages of using this Min-Heap approach for determining 'Top 10 most liked posts' in real-time, streaming data scenarios.

. This is because insertion into or extraction from a heap of size K takes logarithmic time with respect to K. Since K is a fixed, small constant (10 in this case), the per-event processing time is very efficient, almost constant.\\n\\nThe space complexity of this approach is `$O(K) Exam Center

, as the heap only ever stores K elements (the top 10 posts). This is highly memory-efficient, especially for a continuous stream of millions of events.\\n\\nIn contrast, a naive approach of storing all 'like' events for all posts and then sorting them to find the top K would have a time complexity of `$O(N \\\\log N) Exam Center

(where N is the total number of posts/events) or `$O(N) Exam Center

to maintain counts in a hash map and then `$O(M \\\\log M) Exam Center

to sort M unique posts. Its space complexity would be `$O(N)` or `$O(M)` (where M is the number of unique posts), which would grow indefinitely and quickly exhaust memory for a streaming scenario. The Min-Heap approach is vastly more efficient for real-time, streaming data.\"}]},{\"id\":\"group-6\",\"questions\":[{\"id\":6,\"text\":\"Summarize the key advantages of using this Min-Heap approach for determining 'Top 10 most liked posts' in real-time, streaming data scenarios.\",\"type\":\"short-answer\",\"correctAnswer\":\"The key advantages are:\\n\\n1. **Memory Efficiency:** It requires only `$O(K) Exam Center

space, which is constant and independent of the total number of events in the stream. This prevents memory exhaustion in unbounded data streams.\\n2. **Real-Time Processing:** Each event is processed in `$O(\\\\log K) Exam Center

time, allowing for rapid, continuous updates to the 'Top K' list as likes stream in.\\n3. **Scalability:** Since K is fixed and small, the performance doesn't degrade as the total number of 'like' events grows into millions or billions.\\n4. **Constant Top K View:** It provides an always up-to-date view of the top K elements without needing to reprocess or sort all historical data.\"}]}],\"targetAudience\":\"Software Engineers, Computer Science Students, Technical Interview Candidates\",\"creationTime\":1770188463475,\"lastVisited\":1770188463475,\"model\":\"predefined\",\"timeTaken\":0,\"apiProvider\":\"static\"}},\"actionData\":null,\"errors\":null}");