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Design an autocomplete suggestions API | J.P. Morgan Interview Question

Q: Design an autocomplete suggestions API

This question evaluates understanding of string-oriented data structures (e.g., prefix trees/tries), ranked retrieval and dynamic updates, testing skills in efficient prefix search, frequency-based ordering, and lexicographical tie-breaking.

Problem: Autocomplete suggestions (Trie)

You are building an autocomplete feature.

Input

A dictionary of n unique words words[i] (lowercase a–z).
An integer array freq[i] where freq[i] is the usage count of words[i] .
Then q queries. Each query provides:
- a prefix string
- an integer k (number of suggestions requested)

Output

For each query, return up to k suggested completions:

A completion is any word that starts with prefix .
Rank by higher frequency first .
Break ties by lexicographical order .

Follow-up

Support updates:

Operation add(word, delta) increases that word’s frequency by delta (word may be new).
Queries after updates must reflect the new frequencies.

Constraints (reasonable interview assumptions)

1 ≤ n, q ≤ 2e5
1 ≤ |word|, |prefix| ≤ 50
Total characters across all words/queries can be large; solutions should be better than scanning all words per query.

Explain what data structure you would use (e.g., Trie) and implement the required operations.

Problem: Autocomplete suggestions (Trie)

You are building an autocomplete feature.

Input

A dictionary of n unique words words[i] (lowercase a–z).

An integer array freq[i] where freq[i] is the usage count of words[i] .

Then q queries. Each query provides:

a prefix string
an integer k (number of suggestions requested)

Output

For each query, return up to k suggested completions:

A completion is any word that starts with prefix .

Rank by higher frequency first .

Break ties by lexicographical order .

Follow-up

Support updates:

Operation add(word, delta) increases that word’s frequency by delta (word may be new).

Queries after updates must reflect the new frequencies.

Constraints (reasonable interview assumptions)

1 ≤ n, q ≤ 2e5

1 ≤ |word|, |prefix| ≤ 50

Total characters across all words/queries can be large; solutions should be better than scanning all words per query.

Explain what data structure you would use (e.g., Trie) and implement the required operations.

Design an autocomplete suggestions API

Quick Overview

Problem: Autocomplete suggestions (Trie)

Input

Output

Follow-up

Constraints (reasonable interview assumptions)

Submit Your Answer

Design an autocomplete suggestions API

Quick Overview

Problem: Autocomplete suggestions (Trie)

Input

Output

Follow-up

Constraints (reasonable interview assumptions)

Submit Your Answer