314,552 interview questions from 6,000+ companies.
Explain SQL window functions and when to use ROW_NUMBER() versus DENSE_RANK() for ranked ticket analysis.
Explain how clustered and non-clustered indexes differ in storage, lookup behavior, and query performance.
Explain star and snowflake schemas, their tradeoffs, and when to use each in Meta-scale analytics systems.
Tests your ability to compare ETL platforms and choose the right tool for scale and requirements.
Tests your grasp of Spark deployment modes and their operational implications.
Tests your ability to implement quality checks, lineage, and traceability across layered data products.
Tests your approach to evolving schemas without breaking downstream pipelines.
Tests your ability to build reliable incremental ingestion patterns in ADF.
Tests your understanding of physical data layout and its effect on distributed query performance.
Tests your ability to troubleshoot and mitigate memory issues in Spark workloads.
Tests your ability to implement robust data ingestion and cleaning logic in Python for ADLS.
Tests your understanding of transactional storage and how it improves pipeline reliability and debugging.
Tests your ability to diagnose skew and apply practical mitigation strategies in Spark.
Tests your ability to design secure, governed data platforms with proper access controls.
Tests your ability to select the right ADF transformation approach for performance and complexity.
Tests your troubleshooting process and performance optimization skills in ADF.
Tests your ability to design production-grade streaming architectures across Azure and analytics platforms.
Tests your ability to diagnose and optimize complex SQL performance issues.
Tests your ability to design resilient data systems with recovery and availability objectives.
