Straightforward join and aggregate, I got through it fine.
Start by clarifying the table schemas and the definition of 'defect rate' (defects per shipment). Then write a SQL query that aggregates shipments and defects separately for the last 30 days, joins them by carrier, and computes the ratio, ensuring proper handling of carriers with zero shipments.
Pro tip: Use a LEFT JOIN from shipments to defects to avoid dropping carriers with zero defects, and consider using a CTE for readability and to filter dates early for performance.
Ask about the table structures (e.g., shipment_id, carrier_id, defect_id, dates) and confirm the exact definition of defect rate (defects per shipment, not per package).
Apply a date filter to both shipments and defects to only include records from the last 30 days, using the appropriate date column (e.g., shipment_date, defect_date).
Use CTEs or subqueries to count total shipments and total defects per carrier, ensuring defects are counted only for shipments within the same period.
Join the aggregated results on carrier, using a LEFT JOIN from shipments to defects to include carriers with zero defects, and calculate defect_rate = defects / shipments, handling division by zero.
Check for edge cases (e.g., carriers with no shipments), round the defect rate appropriately, and present the final result sorted by carrier or defect rate.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the base query that calculates weekly defect rate per carrier, then use the LAG window function partitioned by carrier and ordered by week to get the previous week's rate. Compute the percentage change as (current_rate - previous_rate) / previous_rate * 100, and handle edge cases like nulls or zero denominators.
Pro tip: Mention that you would validate the results by checking a few carriers manually and consider using NULLIF to avoid division by zero, showing attention to data quality and robustness.
Write a subquery or CTE that aggregates defect counts and total shipments per carrier per week, then calculates the defect rate as defects divided by total shipments.
Use LAG(defect_rate) OVER (PARTITION BY carrier ORDER BY week) to retrieve the previous week's defect rate for each carrier.
Compute the week-over-week percentage change using the formula: (current_rate - previous_rate) / previous_rate * 100. Use NULLIF or CASE to handle division by zero.
Decide how to treat the first week (no previous week) and weeks with zero previous defect rate. Consider returning NULL or 0 for these cases and document the choice.
Show the final query, explain the output columns, and mention how you would validate the results, such as spot-checking a few carriers or comparing with manual calculations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the data sources and definitions: what constitutes a 'Damaged' defect, how origin-destination lanes are identified, and the time frame for 'past quarter'. Then outline a SQL or Python approach to filter defects, group by lane, count, and rank to get the top 3. Finally, discuss how you would validate and present the results, including any caveats.
Pro tip: Mention that you would check for data completeness and potential biases, such as missing lane information or inconsistent defect coding, and propose a quick sanity check by comparing with overall defect rates. This shows you think about data quality and business impact, not just the query.
Ask clarifying questions to ensure you understand the exact defect type, time range, and lane definition. Confirm whether 'past quarter' means the last complete quarter or trailing 90 days.
Determine which tables contain defect records, shipment details, and lane information. Understand how to join them and filter for 'Damaged' defects.
Construct a SQL query that filters defects by type and date, groups by origin-destination lane, counts occurrences, and orders descending to get top 3.
Check for data anomalies, missing values, or outliers. Consider if the top lanes are statistically significant and if there are any confounding factors.
Present the top 3 lanes with counts, and suggest next steps such as root cause analysis or operational improvements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.