Challenge your understanding of hash maps and sets with these essential questions on lookup operations and frequency counting. This quiz covers foundational concepts, key operations, and common scenarios for data structure efficiency, duplicates, and key management.
Given a large collection of unique integers, which data structure is most efficient for determining if a specific value exists, and why?
Explanation: A hash map offers average constant time lookup performance for checking if a key exists, thanks to its hashing mechanism. Arrays and linked lists require linear search for lookup, which is typically slower with large datasets. A stack is not designed for arbitrary element lookup and only allows access to the most recently added element. Therefore, among the options, a hash map is the most efficient for this purpose.
If you need to count how often each word appears in a large text, what kind of information should your hash map store as values?
Explanation: For counting occurrences, a hash map should store word frequency counts as values, mapping each word to its number of appearances. Boolean flags only indicate presence or absence, not frequency. Unique indexes point to locations, not counts, and sorted lists are unnecessary for simple counting. Thus, frequency counts are best suited for this case.
What is the main property of a set as a data structure when adding elements, demonstrated when inserting numbers like 5, 8, and 5 again?
Explanation: Sets ensure that only unique elements are stored, so inserting the number 5 twice results in only one instance of 5 being present. Maintaining sequence order is not guaranteed in sets, and automatic sorting is not a property of typical sets. Duplicates are not permitted by definition, making uniqueness the correct answer.
When two different keys produce the same hash index in a hash map, which term describes a typical method to resolve this?
Explanation: Collision handling refers to strategies used to manage situations where keys map to the same index, such as chaining or open addressing. 'Singleton chaining' and 'Prime bucketing' are not standard terms for this process and may even be incorrect phrase variants. 'Array shuffling' is unrelated to hash collision management.
If a hash map stores letters as keys and their occurrence counts as values from a list like ['a', 'b', 'a', 'c', 'b'], which standard process finds the most frequent letter?
Explanation: To find the most frequent letter, you need to iterate through the hash map's values and keep track of the maximum count and corresponding key. Adding all values gives a total count, not the frequency of a specific letter. Sorting the keys only organizes them, and reversing key-value pairs is irrelevant for finding the maximum. Thus, value iteration is correct for this task.