Real questions from top companies in SQL
Data Warehouse Design from scratch
Describe a challenging project where you optimized a complex ETL process.
Describe a situation where you had to redesign a data model to meet changing business needs
Design a Custom API that can query a backend server and return customer data such as the number of orders placed by a user based on their user ID
Design a daily ETL pipeline to ingest API data into BigQuery.
Design a financial database system focusing on database models, schema design, partition keys, and query optimization techniques.
Design a relational data model for a sales database, incorporating normalization techniques
Design a structure (data model) that allows efficient querying of movies based on multiple search criteria (title, genre, actor, director).
Design the data model for an ETL pipeline that ingests data from a database and loads it into Snowflake
Designing backend architecture for SQL Warehouse?
Designing scalable data models - explain approach
Difference Between Truncate/Delete and Union/Union All – Performance and Usage
Explain BigQuery Architecture.
Explain CTE vs Temp Table. What are the differences and use cases?
Explain Data Modeling SCD Types (Type 1, 2, 3).
Articulate the architectural decisions, scalability trade-offs, and cost implications of designing an AWS data platform. How would you justify glue vs. EMR, Redshift vs. Athena, and when would each choice become cost-prohibitive at scale?
Explain the architectural rationale for using LeftAntiJoin vs. NOT IN vs. NOT EXISTS in a distributed context. When does LeftAntiJoin become a performance or scalability bottleneck, and how do broadcast vs. shuffle joins affect cost?
Describe a cross-team data project where you had to align architectural boundaries, ownership, and SLAs. How did you handle conflicting priorities, technical debt, and the scalability of communication as the number of stakeholders grew?
Walk through a production incident where data freshness or correctness was at risk. How did you balance immediate mitigation vs. root-cause remediation? What architectural changes would prevent recurrence, and what are the cost vs. reliability trade-offs?
Explain the architectural trade-offs when optimizing a query on 100M+ rows: indexing vs. partitioning vs. materialized views. When does each approach become cost-prohibitive or operationally burdensome, and how do you quantify impact?
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