Trieshard70 minAI/ML Engineer

Embeddings Prefix Intent Index

AI/ML Engineer signal: trie + prefix counts in a embedding drift monitor context. This is a ProdMatch-owned ai ml engineer drill, framed as a March 2026 NVIDIA Model Observability simulation, not a copied platform question.

Company context

NVIDIA · Model Observability

Freshness

March 2026

Product surface

embedding drift monitor

ProdMatch interview simulation based on product-team patterns; not a claim of a real company question.

Question

Build a prefix index for embedding drift monitor. Support insert(term, weight), delete(term), and topPrefix(prefix) returning the best active term by weight then lexicographic order.

Input

A sequence of index operations.

Output

Return values for topPrefix operations.

Constraints

1 <= operations <= 200000
Total characters <= 1000000
Weights can change through reinsertion.

Concepts

vector search
RAG retrieval
recommendation graphs
trie
prefix counts
autocomplete

insert pay 5, insert payout 7, topPrefix pa -> payout

Approach

Try framing your own approach first. The 30 seconds you think before peeking is where learning happens.

Clean Solution

Reveal the approach first.

How well did you understand?

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Common Mistakes

Deletion is hard if nodes cache best child; use lazy invalidation or per-node heap when deletes are frequent.
Tie-break lexicographically for deterministic output.

Next Similar Problems

Vector Prefix Intent Indexhard GraphRAG Prefix Intent Indexmedium Recommend Prefix Intent Indexmedium