Start by aggregating revenue per item within each category, then rank items by revenue using a window function like ROW_NUMBER() or RANK(). Filter to the top 10 per category, compute the sum of their revenue, and divide by the total category revenue to get the percentage. Use CTEs for clarity and to avoid repeating subqueries.
Pro tip: Mention that you would clarify the definition of 'top 10' (e.g., ties handling) and whether revenue should be calculated before or after discounts/returns, as these details matter in a real Amazon interview. Also, consider performance implications for large datasets and suggest indexing or partitioning strategies.
Ask about the revenue definition (e.g., gross vs net), tie-breaking rules for ranking, and whether the query should be dynamic for any category. Confirm the expected output format.
Write a subquery or CTE that groups by category and item, summing revenue (e.g., quantity * price) to get total revenue per item.
Use a window function like ROW_NUMBER() or RANK() OVER (PARTITION BY category ORDER BY revenue DESC) to assign ranks. Decide whether to use RANK (handles ties) or ROW_NUMBER (arbitrary tie-break).
Filter to ranks <= 10, then compute the sum of revenue for these top items per category. Also compute the total revenue per category (using a separate CTE or window function).
Join the top-10 sum and total sum per category, then calculate (top10_sum / total_sum) * 100 as the percentage. Output category, item, revenue, and percentage.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Gave a textbook answer about reducing redundancy vs optimizing reads.
Start by clearly defining normalization and denormalization, then contrast their trade-offs in terms of data integrity, query performance, and storage. Finally, explain when to choose each based on the use case, emphasizing that the decision depends on read vs. write patterns and system requirements.
Pro tip: At Amazon, always tie your answer to specific business needs—like high-scale read-heavy systems or transactional integrity—and mention how you'd measure the impact of your choice (e.g., query latency, storage cost).
Explain that normalization organizes data to minimize redundancy and dependency by dividing tables and defining relationships, typically following normal forms (1NF, 2NF, 3NF).
Describe denormalization as intentionally introducing redundancy by combining tables or adding redundant data to improve read performance and simplify queries.
Contrast the trade-offs: normalization ensures data integrity and reduces storage but can slow reads due to joins; denormalization speeds up reads but risks inconsistency and increases storage.
Choose normalization for write-heavy, transactional systems (OLTP) where data consistency is critical and storage optimization matters.
Choose denormalization for read-heavy, analytical systems (OLAP) or when query performance is paramount, such as in data warehousing or real-time dashboards.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Covered extraction, schema validation, transformations like deduplication and type casting, loading into a warehouse, and basic monitoring.
Start by clarifying the dataset's schema, volume, and business requirements, then outline an end-to-end ETL pipeline covering ingestion, transformation, and loading. Emphasize data quality, scalability, and how the pipeline supports downstream analytics and machine learning at Amazon's scale.
Pro tip: Highlight how you would incorporate data validation and monitoring at each stage, and mention specific AWS services like Glue, Redshift, or S3 to show practical cloud expertise. Also, discuss how you would handle late-arriving data and ensure idempotency.
Clarify business goals, data sources, update frequency, and data quality expectations. Profile the sales dataset to understand schema, volume, and anomalies.
Design ingestion from various sources (e.g., databases, APIs, flat files) using batch or streaming approaches. Ensure scalability, fault tolerance, and data integrity.
Apply transformations: deduplication, normalization, enrichment, aggregation, and business logic. Implement data quality checks and handle missing or invalid data.
Load processed data into a target warehouse (e.g., Redshift) or data lake (e.g., S3) optimized for query performance. Consider partitioning, indexing, and compression.
Use workflow tools (e.g., Airflow, Step Functions) to schedule and monitor pipelines. Set up alerts for failures, data quality issues, and performance bottlenecks.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.