Real questions from top companies · hard
Architect a solution to handle notifications for millions of users with varying preferences.
Build a banking system architecture from scratch, highlighting critical workflows, scalability, and data management strategies.
Business Role of Data Pipeline
CAP Theorem
CI/CD implementation across environments (DEV, QA, UAT, PreProd, PROD)
Can Schema Evolution lead to data inconsistencies? If so, how do you manage them?
Compare Native vs Cloud Database Systems.
Data Volume in Pipelines and Scalability Solutions
Demonstrate system design principles applied to BI solutions.
Describe a data pipeline you built and optimized.
Describe a fault-tolerant distributed data processing system.
Describe a strategy for implementing a real-time content delivery monitoring system.
Describe a system design to handle product launches with massive traffic spikes.
Describe an end-to-end data pipeline project you worked on, highlighting your role and the technologies used.
Describe handling schema evolution in AWS Redshift without downtime.
Describe how Kafka ensures data durability and fault tolerance.
Describe how data is ingested, transformed, and served in a data pipeline.
Describe how to monitor and log errors effectively in a real-time data pipeline.
Describe how you would architect a pipeline to process real-time logs with schema evolution
Describe how you would debug a failing ETL pipeline in production.
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