Start by clarifying the schema and requirements, then outline a SQL query that groups by status and computes aggregates. For the failure reasons, normalize them (e.g., lowercase, trim) and aggregate into a frequency-ordered list using string aggregation functions. Finally, discuss performance considerations and potential edge cases.
Pro tip: Mention that normalizing failure reasons should be done consistently, and consider using a CTE to pre-aggregate failure reasons before joining to avoid duplication. Also, highlight that ordering by frequency within the aggregated list requires a subquery or window function.
Confirm the table structure, data types, and what 'normalized' means (e.g., lowercase, remove punctuation). Ask about expected output format for the aggregated list (e.g., comma-separated string, JSON array).
Write a subquery or CTE that groups by status and normalized failure_reason, counts occurrences, and orders by count descending. This prepares the data for aggregation into a list.
In the main query, group by status to calculate total transaction count and total amount. Ensure you handle NULLs appropriately (e.g., COALESCE for amount).
Use a string aggregation function (e.g., STRING_AGG in PostgreSQL, GROUP_CONCAT in MySQL) to concatenate the ordered failure reasons with their counts into a single string per status.
Consider indexing on status and failure_reason for performance. Validate the query with sample data and discuss potential pitfalls like duplicate reasons due to case sensitivity.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.