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Straightforward join on member id, filter claim_date to 2024, then group by age_band and gender.
Start by clarifying the table schemas and business definitions (e.g., age band boundaries, gender categories, paid claim amount). Then outline a query that joins membership to claims, filters for 2024 paid claims, computes age bands from date of birth, and groups by age band and gender to sum paid amounts.
Pro tip: Mention that you would validate the age band logic against business rules and consider edge cases like missing gender or date of birth, ensuring the query handles them gracefully (e.g., using COALESCE or excluding them with a note).
Ask about the schema: membership table (member_id, date_of_birth, gender, etc.) and claims table (claim_id, member_id, paid_amount, service_date, etc.). Confirm definitions: what constitutes a 'paid' claim, how age bands are defined (e.g., 0-17, 18-34, etc.), and whether to include only members with claims or all members.
In the claims table, filter rows where the service date (or payment date) falls in 2024 and the claim is paid (e.g., paid_amount > 0 or a status flag). This reduces the dataset before joining.
Join the filtered claims to the membership table on member_id. Calculate age as of the claim date (or a fixed date like end of 2024) using date of birth, then bucket into predefined age bands using a CASE statement.
Group the joined data by the computed age band and gender, and sum the paid_amount to get total paid claim amounts. Use appropriate aggregation functions and aliases.
Check for NULLs in gender or date_of_birth and decide how to handle them (exclude or label as 'Unknown'). Consider indexing on join and filter columns for performance, and verify results with a sanity check (e.g., total sum matches overall total).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where it got a bit more interesting.
Clarify the definition of age band and whether to use member age at claim or at year start, then aggregate total paid claim amounts by year and age band. Compute year-over-year changes and present the trend using a line chart or table, highlighting key patterns and anomalies.
Pro tip: Always validate that the age band assignment is consistent across years to avoid artificial trends due to members aging into different bands; consider using a fixed cohort or adjusting for age to isolate true cost trends.
Confirm with the interviewer the definition of 'age band' (e.g., 0-17, 18-34, etc.), whether to use member age at the time of claim or at the start of the year, and the time period for 'all years'.
Filter claims to paid amounts, assign each claim to an age band based on the chosen age definition, and aggregate total paid amounts by year and age band. Ensure data quality by handling missing or negative paid amounts.
For each age band, compute the year-over-year change in total paid amounts (absolute and percentage). Consider using a lag function or self-join to compare consecutive years.
Create a line chart with years on the x-axis, total paid amounts on the y-axis, and separate lines for each age band. Identify upward/downward trends, seasonality, or outliers, and discuss potential drivers.
Summarize the key trends, note any data limitations (e.g., incomplete years, changes in claim processing), and suggest next steps such as adjusting for inflation or member mix.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.