Real questions from top companies · hard
What is the difference between Pandas DataFrame and Spark DataFrame? When would you prefer using each?
What is the importance of the checkpoint location in Databricks?
What is the salting technique, and when would you use it?
What performance optimization techniques have you applied in Spark, Sqoop, or Databricks?
What role does Kafka play in real-time data streaming pipelines?
What role would Kafka or similar event-driven platforms play in your architecture?
What strategies would you use to reduce latency in a streaming data pipeline?
What trade-offs would you consider when choosing between batch processing and real-time streaming?
When submitting Spark jobs, how does the process work in the backend? Explain.
Why I chose specific technologies (e.g., Spark over traditional ETL tools)
Write a PySpark script to check for missing values and duplicate rows in a DataFrame. How would you ensure data quality before saving it to a storage system?
Write a Spark job to count word occurrences from an S3 dataset.
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
Type or paste your answer to any of these questions and our AI Coach scores it, highlights gaps, and rewrites it at FAANG quality. Free to try.