Top-K Frequent Words: Hash Maps u0026 Min-Heap Fundamentals Quiz

Test your knowledge of finding the top-K frequent words in a text corpus using hash maps and min-heaps. This quiz covers key concepts, usage scenarios, and time-space trade-offs in designing efficient solutions for word frequency analysis.

1. Identifying the Most Common Words

   When finding the top 3 most frequent words in the sentence 'apple apple orange banana banana banana', which data structure is best to first count the frequency of each word?

   1. Trie
   2. Hash Map
   3. Stack
   4. Queue

2. Purpose of the Min-Heap

   Why is a min-heap commonly used after building a hash map of word frequencies to find the top-K frequent words in a large corpus?

   1. It efficiently maintains the K largest elements seen so far
   2. It deletes duplicate words automatically
   3. It stores all possible substrings for searching
   4. It sorts the entire list of words in ascending order

3. Space Efficiency Concern

   What is the space complexity of storing all unique words and their counts from a text with N total words and W unique words using a hash map?

   1. O(N^2)
   2. O(K)
   3. O(W)
   4. O(log N)

4. Extracting Top-K Words

   After filling a hash map with word frequencies, inserting each entry into a min-heap of size K gives what worst-case time complexity for the heap operations?

   1. O(W log K)
   2. O(W log W)
   3. O(K log W)
   4. O(K log N)

5. Limitation of Using a Heap Only

   Why can't you use only a min-heap to count word frequencies in a corpus without a hash map first?

   1. A heap overwrites all existing data upon insertion
   2. A heap cannot track individual word counts before insertion
   3. A heap is slower than a hash map for lookups
   4. A heap does not allow any form of sorting

6. Big-O Lower Bound

   When analyzing all the words in a text of N words to find word frequencies, what is the minimum possible time complexity?

   1. O(log N)
   2. O(1)
   3. O(N^2)
   4. O(N)

7. Time Complexity of Building the Hash Map

   Given a text of length N, what is the time complexity to construct the hash map with counts for each unique word?

   1. O(2^N)
   2. O(N log N)
   3. O(K^2)
   4. O(N)

8. Choosing Data Structures for Top-K Extraction

   Which of the following combinations is most efficient for finding top 5 frequent words in a large dataset?

   1. Array for counting, then bubble sort
   2. Queue for input, then selection sort
   3. Trie for storing, then stack for extraction
   4. Hash map for counting, then min-heap of size 5

9. Impact of K on Memory Usage

   In the two-step process, increasing the value of K (number of frequent words to retrieve) will affect the memory used by which data structure?

   1. Input String
   2. Min-Heap
   3. Stack
   4. Hash Map

10. Heap Size and Performance

    For the operation of keeping the top K frequent words, why is the min-heap typically set to size K rather than W (number of unique words)?

    1. To avoid using a hash map at all
    2. To limit memory usage and improve performance
    3. So all words can be sorted alphabetically
    4. Because K is always equal to W