Real questions from top companies · hard
Cloud Architecture - explain
Consolidate hotel reviews and create a dashboard. Design a data model for the reviews.
Create Spark Session, read CSV, join, and write as table. Provide example code.
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.
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?
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