Hash Table Questions at Infosys: What to Expect

Hash Table questions appear in 17% of Infosys coding problems, making them a core data structure you must master. Their interviews often assess practical problem-solving rather than obscure theory, and hash tables are the go-to tool for optimizing lookups, counting elements, and managing data relationships efficiently. A strong grasp here demonstrates you can write performant, clean code—a key expectation for roles involving system maintenance, data processing, or application development at Infosys.

What to Expect — Types of Problems

Infosys hash table problems typically fall into three categories:

1. Frequency Counting: The most common type. You'll be asked to count occurrences of elements (characters in a string, numbers in an array) to find duplicates, anagrams, or unique items.
2. Pair Finding: Problems where you need to find two elements that satisfy a condition, like a pair summing to a target value. The hash table stores seen elements for instant lookup.
3. Data Mapping: Using a hash table as a mapping tool to translate or group data, such as storing employee IDs to names or grouping transactions by type.

The problems are generally of easy to medium difficulty, focusing on correct application of the pattern rather than complex algorithmic twists.

How to Prepare — Study Tips with One Code Example

Focus on understanding the core pattern: trade space for time. Use a hash table (dictionary, object, or HashMap) to store intermediate results, turning O(n²) nested loops into O(n) single passes.

Practice identifying when a problem needs a hash table. Key signals include: "find a pair," "check if already seen," "count frequencies," or "group by property."

Master this classic example: Two Sum. Given an array of integers and a target, return the indices of the two numbers that add up to the target.

def two_sum(nums, target):
    seen = {}
    for i, num in enumerate(nums):
        complement = target - num
        if complement in seen:
            return [seen[complement], i]
        seen[num] = i
    return []

# Example: two_sum([2, 7, 11, 15], 9) returns [0, 1]

The pattern is identical across languages: store each element as you iterate, and check if its needed complement is already in the map.

Recommended Practice Order

1. Start with basic frequency counting (e.g., "First Unique Character in a String").
2. Move to the Two Sum problem and its variants.
3. Practice anagram detection using character count maps.
4. Tackle problems that involve grouping, like grouping transactions or categorizing data.

Build fluency by writing the code for the same problem in multiple languages if you're applying for a role with a specific tech stack.

Practice Hash Table at Infosys