My first instinct was to write something flexible that could handle any grouping key, which was the wrong move.
Start by clarifying the requirements: what key to aggregate by, which summary fields are needed, and how to handle edge cases like missing data. Then outline a solution using a hash map to group records by the key, iterating through the list once to accumulate totals, counts, and weighted sums. Finally, compute derived metrics like weighted averages and discuss trade-offs such as time/space complexity and potential optimizations.
Pro tip: Demonstrate awareness of data quality issues: mention how you'd handle nulls, zero principal, or invalid rates, and propose validation or default values. Also, discuss scalability: for large datasets, consider streaming aggregation or parallel processing.
Ask questions to confirm the aggregation key, required summary fields (e.g., total principal, count, weighted average rate), and any constraints like memory limits or data cleanliness.
Choose a hash map (dictionary) to group records by the key, with each value being an accumulator object or struct that holds running totals, counts, and weighted sums.
Iterate through the loan records once, updating the accumulator for each record's key: add to totals, increment counts, and accumulate weighted sums (e.g., principal * rate for weighted average).
After the pass, compute final summary fields like weighted average rate by dividing the weighted sum by the total weight (e.g., total principal), handling division by zero.
Analyze time and space complexity (O(n) time, O(k) space where k is number of groups), and address edge cases such as empty input, null values, and numerical precision.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the matching rules and data schemas, then design an efficient algorithm that indexes loans by borrower ID and uses a sliding window or interval tree for date ranges. Process transactions in order, match to the best loan based on amount and date proximity, and maintain per-loan histories while collecting unmatched transactions for review.
Pro tip: Mention that in real-world financial systems, exact matches are rare due to timing and amount discrepancies, so you'd implement a scoring system with tolerance thresholds and log unmatched records for manual reconciliation.
Ask about the exact matching criteria: borrower ID exact match, date window definition (e.g., ±N days), and amount tolerance (exact or range). Confirm output format for per-loan history and unmatched records.
Index loans by borrower ID, and for each borrower, store loans in a structure that allows efficient date range queries (e.g., sorted list or interval tree). Also, prepare a map from loan ID to a list of transactions for history.
For each transaction, retrieve candidate loans for the borrower, filter by date window and amount criteria, and select the best match (e.g., closest date or exact amount). If no match, add to unmatched list.
Define tie-breaking rules (e.g., earliest loan, smallest amount difference) and consider multiple transactions matching the same loan. Ensure unmatched records are clearly flagged with reasons.
Produce per-loan transaction histories and a list of unmatched transactions. Validate by checking counts, sums, and spot-checking matches to ensure correctness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.