Spent the first couple minutes just making sure I understood what 'analyze' meant here because that word can mean a lot of things.
Start by clarifying the schema and requirements (e.g., what defines a transaction, time granularity, and desired metrics). Then outline a SQL query that groups transactions by month and customer, computes aggregates, and uses window functions to analyze trends and detect anomalies. Finally, discuss how to handle edge cases and optimize the query for large datasets.
Pro tip: Mention that you would validate the query with sample data and consider using date truncation functions (like DATE_TRUNC) to ensure consistent monthly grouping across different SQL dialects. Also, highlight the importance of indexing on transaction date and customer ID for performance.
Ask questions to understand the table structure, what constitutes a transaction, and what specific insights are needed (e.g., total spend, transaction count, month-over-month growth).
Write a SQL query that groups transactions by customer and month, calculating aggregates like SUM(amount), COUNT(*), and AVG(amount). Use DATE_TRUNC or equivalent to extract the month.
Use window functions (e.g., LAG, LEAD) to compute month-over-month changes, moving averages, or cumulative sums to identify trends.
Apply statistical methods (e.g., z-score, IQR) or simple thresholds to flag unusual spikes or drops in transaction metrics.
Discuss indexing, partitioning, and query performance considerations. Suggest validating results with sample data or unit tests.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.