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DASome real-time interview questions on Databricks that cover architecture, optimisation, integration, and troubleshooting:
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1. You notice one of your Spark jobs on Databricks is running significantly slower than usual after a recent data volume increase. How do you diagnose and fix the problem?
2/ A Delta Lake table update fails with a concurrent write conflict error. How do you handle such concurrency issues in production?
3/ You have a join between a large table and a very small table, but the job is still running slow. What steps do you take to optimise the join in Databricks?
4/ Your Spark job is failing intermittently with OutOfMemoryError on the executors. How do you troubleshoot and mitigate this issue?
5/ Explain how you would handle a scenario where a large partition in a Delta table causes data skew and slows down the job execution.
6/ You are tasked with designing a streaming pipeline in Databricks to ingest IoT sensor data with low latency. What architecture and optimisations would you apply?
7/ Your Databricks cluster frequently auto-terminates during heavy workloads, causing job failures. How do you adjust cluster settings to handle this?
8/ A scheduled Databricks job fails with permission denied errors accessing S3 or ADLS storage. How would you troubleshoot and fix access issues?
9/ You deployed a Databricks notebook that queries data from multiple external sources (e.g., JDBC databases, APIs), but the performance is poor. What approaches would you take to improve this?
10/ Delta Lake’s OPTIMIZE command is taking too long on a large table. How do you approach optimising this command without impacting production workloads?
Credit : Abhinav singh
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