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This question is commonly asked for Software Engineer candidates at Plaid during technical interviews.

Resolve routing-number to bank mapping

Company: Plaid

Role: Software Engineer

Category: Coding & Algorithms

Difficulty: easy

Interview Round: Technical Screen

You are given bank-routing data coming from one or more JSON sources. Each source provides a list of records like: ```json { "routing": "026009593", "bank_name": "Bank of America" } ``` Complications: - The same bank may appear under multiple names (e.g., "Bank of America", "BoA", "BofA"). - The same routing number may appear multiple times (even within a source). - Across multiple sources, the same routing number may map to different banks (conflicts). Task: 1) Normalize bank names using a provided alias mapping (e.g., a dictionary that maps each observed name to a canonical bank id/name). 2) Produce a final mapping `routing -> canonical_bank`. 3) If multiple sources disagree for the same routing number, resolve it using this policy: - Choose the canonical bank that appears in the most sources for that routing number (majority vote across sources). - If there is a tie, break ties by a given source-priority order (e.g., SourceA > SourceB > SourceC). 4) Also output a list of routings that had conflicts (i.e., more than one canonical bank was claimed across sources), along with the votes that led to the decision. Write a function that takes: - `sources: List[List[Record]]` (each inner list is one source’s records) - `alias_to_canonical: Map[str, str]` - `source_priority: List[int]` (or equivalent) and returns the resolved mapping and the conflict report.

Quick Answer: This question evaluates a candidate's skills in data normalization, deduplication, aggregation, and conflict resolution across multiple JSON data sources, with emphasis on canonicalization of aliases and handling inconsistent mappings.

Part 1: Normalize Routing-Number Bank Mappings

You are given raw bank mapping records loaded from JSON. Each record contains a routing number and a bank name. You are also given an alias dictionary where one canonical bank can have several alternative names. Normalize bank names case-insensitively, trim extra spaces, and build a final routing-number-to-bank mapping. If the same routing number appears multiple times and all names resolve to the same canonical bank, keep that bank. If a routing number resolves to different canonical banks, store 'CONFLICT' for that routing number. If a bank name does not appear in the alias dictionary, use its normalized lowercase form as its own canonical name.

Constraints

0 <= len(records) <= 100000
0 <= total number of alias strings across all banks <= 100000
Each record contains keys 'routing_number' and 'bank_name'
Routing numbers should be treated as strings in the output
After normalization, alias groups do not overlap

Examples

Input: ([{'routing_number': '111000025', 'bank_name': ' Bank of America '}, {'routing_number': '111000025', 'bank_name': 'BOFA'}, {'routing_number': '021000021', 'bank_name': ' chase '}, {'routing_number': '021000021', 'bank_name': 'JPMORGAN CHASE'}], {'Bank of America': ['BOFA', 'B.O.A.', 'Bank of america'], 'Chase': ['JPMorgan Chase', 'JP Morgan Chase']})

Expected Output: {'111000025': 'Bank of America', '021000021': 'Chase'}

Explanation: Both records for 111000025 resolve to Bank of America, and both records for 021000021 resolve to Chase after case-insensitive normalization and alias matching.

Input: ([{'routing_number': '111', 'bank_name': 'alpha bank'}, {'routing_number': '111', 'bank_name': 'Beta Bank'}, {'routing_number': '111', 'bank_name': ' ALPHA BANK '}], {'Alpha Bank': ['Alpha bank'], 'Beta Bank': ['beta bank']})

Expected Output: {'111': 'CONFLICT'}

Explanation: The same routing number resolves to Alpha Bank and Beta Bank, so the final value must be CONFLICT. Once conflicted, it stays conflicted.

Hints

Build one reverse hash map from every known alias to its canonical bank name before processing the records.
For each routing number, remember the first resolved bank. If a later resolved bank is different, mark that routing number as a conflict.

Part 2: Resolve Routing Numbers Across Multiple Data Sources

You are given several data sources that each map routing numbers to bank names. Different sources may disagree, and the same source may even contain inconsistent records for the same routing number. You are also given a bank alias dictionary. Resolve each routing number using weighted voting across sources. First normalize bank names using the alias dictionary, trimming spaces and ignoring case. Inside a single source, if a routing number appears multiple times with equivalent names, that source casts one vote for that bank. If a single source contains conflicting banks for the same routing number, that source casts no vote for that routing number. Across sources, add up the source weights for each candidate bank. Return the bank with the highest total weight. If there is a tie for highest weight, or if no valid source votes remain for a routing number that appeared in the input, return 'AMBIGUOUS'.

Constraints

0 <= len(sources) <= 10000
The total number of records across all sources is at most 100000
Each source contains keys 'source', 'weight', and 'records'
Each weight is a positive integer
After normalization, alias groups do not overlap

Examples

Input: ([[('111', ' Bank of America '), ('111', 'boa')], [('111', 'Bank of America')]], [2, 1], {'boa': 'Bank of America'})

Expected Output: {'111': 'Bank of America'}

Explanation: In the first source, both names normalize to Bank of America, so that source casts one vote worth 2. The second source adds another vote worth 1. Bank of America wins with total weight 3.

Input: ([[('222', 'Chase'), ('222', ' JP Morgan Chase ')], [('222', 'Wells Fargo')], [('222', ' chase ')]], [3, 4, 2], {'jp morgan chase': 'Chase'})

Expected Output: {'222': 'Chase'}

Explanation: Source 1 contributes 3 to Chase because both records are equivalent after alias normalization. Source 2 contributes 4 to Wells Fargo. Source 3 contributes 2 to Chase. Final totals: Chase 5, Wells Fargo 4.

Hints

Resolve duplicates and contradictions inside each source before doing any cross-source voting.
Use a nested hash map like routing -> bank -> total score to accumulate weighted votes.

Quick Overview

This question evaluates a candidate's skills in data normalization, deduplication, aggregation, and conflict resolution across multiple JSON data sources, with emphasis on canonicalization of aliases and handling inconsistent mappings.