The memo parsing tripped me up more than I expected.
Start by clarifying the input formats and matching rules, then outline a solution that parses the payment memo using the specified prefix, performs a dictionary-based lookup for the invoice, and returns a structured result or a well-defined error. Emphasize edge cases, complexity, and how you would validate the implementation.
Pro tip: Proactively discuss how you would handle malformed memos, duplicate invoice IDs, and missing invoices with clear error types, and mention that you would write unit tests for these scenarios to ensure robustness.
Ask about the exact prefix format, invoice ID structure, payment memo examples, and expected error handling. Confirm whether multiple invoices can match or if IDs are unique.
Propose using a hash map (dictionary) to store invoices keyed by ID for O(1) lookup. Discuss how to parse the memo efficiently, e.g., using string operations or regex.
Describe step-by-step: extract the ID from the memo after the prefix, look up the invoice in the map, and return a structured match object (e.g., invoice ID, payment ID, amount) or an error if not found.
Cover scenarios like missing prefix, empty ID, non-existent invoice, duplicate payments, and malformed memos. Define clear error types or messages for each.
State time and space complexity (O(n) preprocessing, O(1) per lookup). Discuss trade-offs between regex and manual parsing, and between returning errors vs. exceptions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First, clarify the matching logic and data structures: parse payments, extract memo prefixes, and for those without a prefix, group invoices by amount. Then, for each amount group, select the invoice with the earliest due date, ensuring deterministic tie-breaking. Finally, design a structured result format that includes matched invoice details and match type for all payments.
Pro tip: Mention that you would handle edge cases like multiple invoices with the same amount and due date by adding a secondary tie-breaker (e.g., invoice ID) to ensure deterministic results, and discuss the time complexity of your approach.
Ask clarifying questions about memo prefix format, amount matching tolerance, and what to do if no invoice matches. Confirm the expected output structure for all payments.
Propose using a hash map to group invoices by amount, and for each group, sort by due date to pick the earliest. For memo-prefixed payments, use direct lookup. Outline the overall flow.
Write pseudocode or explain step-by-step: first attempt memo prefix match; if not found, fall back to amount-based match using the precomputed map. Ensure each payment gets a structured result.
Discuss time and space complexity (e.g., O(P + I log I) for sorting invoices per amount group). Mention trade-offs between pre-sorting and on-the-fly selection, and scalability for large datasets.
Propose test cases: payments with and without memo prefix, multiple invoices with same amount, no matching invoice, and ties in due date. Explain how to verify correctness and handle failures.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Felt like a softer question after the coding parts.
Start by clarifying the two matching modes (e.g., exact vs. fuzzy) and the reconciliation goal, then propose a unified output schema that captures matched, unmatched, and error cases with clear status flags and metadata. Emphasize trade-offs between simplicity, extensibility, and performance, and how the structure supports downstream analysis and debugging.
Pro tip: Design the output to be self-describing: include a 'match_type' field and a 'confidence_score' even for exact matches, so downstream consumers can filter and audit without re-running the logic. Also, consider partitioning the output by status to optimize query performance in BigQuery.
Ask about the two matching modes (e.g., exact vs. fuzzy) and the expected volume, latency, and consumers of the reconciliation results. Confirm whether errors include data quality issues or only matching failures.
Propose a single table with columns like record_id, source, match_status (matched/unmatched/error), match_type, confidence_score, error_reason, and timestamps. Ensure it accommodates both modes without schema changes.
For matched records, include both source and target keys, match_type (e.g., exact, fuzzy), and a confidence score. Consider adding a match_group_id to link multiple records that refer to the same entity.
For unmatched, include the record and a reason (e.g., no candidate). For errors, include error_code and error_message. Ensure these are easily filterable and don't break downstream aggregations.
Compare a wide table vs. separate tables for each status. Discuss performance implications (e.g., partitioning by status) and how the schema supports future matching modes or additional metadata.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.