Real questions from top companies · hard
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.
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?
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?
Explain bloom filters in Spark: how they reduce I/O and when they introduce false positives that hurt performance. What are the scalability and cost implications of enabling dynamic partition pruning and bloom filter pushdown at petabyte scale?
Design a star schema for retail analytics (e.g., Adidas). Explain the dimensional modeling choices, SCD strategy, and how you would scale this schema for global multi-currency, multi-region deployments. What are the refresh and storage cost implications?
Explain the Medallion Architecture (Bronze, Silver, Gold).
Given a CSV file with raw customer transactions, design an ETL pipeline that cleans data, aggregates total sales by region and product, and loads into target table
How can you automate data insertion into BigQuery using Python?
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