Hash Table Questions at Capgemini: What to Expect

Hash Table questions appear in roughly one-third of Capgemini's coding problems. This frequency signals their practical importance: real-world software development constantly involves data lookup, caching, and deduplication—core operations where hash tables (or hash maps/dictionaries) excel. For a global consulting and technology services firm like Capgemini, efficient data handling is non-negotiable in client projects. Mastering hash tables demonstrates you can write performant, clean code for common business logic, making it a reliable filter for technical interviews.

What to Expect — Types of Problems

Capgemini's hash table problems typically focus on practical applications rather than theoretical implementation. You can expect two main categories:

1. Frequency Counting: The most common pattern. Problems involve counting occurrences of elements (characters in a string, numbers in an array) to find duplicates, anagrams, or majority elements.
2. Mapping and Lookup: Using a hash table to store mappings for fast access. This includes problems like two-sum (finding a pair that adds to a target), storing relationships, or memoization to optimize recursive calls.

Problems are often framed in straightforward scenarios: "Find the first non-repeating character," "Check if two arrays are similar," or "Determine if two strings are anagrams." The challenge lies in implementing an O(n) solution efficiently.

How to Prepare — Study Tips with One Code Example

Focus on mastering the frequency counting pattern. It's the workhorse for most hash table questions. Follow these steps:

1. Identify the element to track (character, number, etc.).
2. Use a hash table/dictionary to store the element as the key and its count as the value.
3. Traverse the input once to build the count.
4. Traverse again or check the map to solve the problem.

A classic example is Checking for Anagrams. Two strings are anagrams if they contain the same characters in the same frequencies.

def is_anagram(s: str, t: str) -> bool:
    if len(s) != len(t):
        return False

    count = {}
    # Build frequency map for string s
    for char in s:
        count[char] = count.get(char, 0) + 1

    # Decrement using string t
    for char in t:
        if char not in count or count[char] == 0:
            return False
        count[char] -= 1

    return True

Recommended Practice Order

Build competence progressively:

1. Start with basic frequency counting (e.g., "First Unique Character in a String").
2. Move to the two-sum pattern and its variants.
3. Tackle problems using hash tables for optimization, like checking duplicates within a range.
4. Finally, combine hash tables with other concepts, such as using them alongside two pointers or sliding windows.

Consistently write the code; don't just understand it. Time yourself to simulate interview conditions.

Practice Hash Table at Capgemini