#Databricks Interview Questions

Some real-time interview questions on Databricks that cover architecture, optimisation, integration, and troubleshooting: Save and share before it disappear 🫠 Follow and check out other career content!! 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 . . #AWS #dataengineering #SQL #DataScience #InterviewPreparation #DataAnalytics #dataengineering #career #careers #Powerbi #analytics #dataanalyst #freshers #dataanalysis #tech #technology #amazon #google #coding #reels #engineering #corporate #SQLChallenges #questions #dataanalyst #TechSkills #softwaredeveloper #software #viralvideos #computerscience #dsa Data Analytics Data Engineering Data bricks

𝗖𝗼𝗺𝗽𝗮𝗻𝘆: 𝗧𝗶𝗴𝗲𝗿 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲: 3–5 Years | 𝗥𝗼𝘂𝗻𝗱: Level 2 Hi everyone 👋 Bookmark this if you have an 𝗔𝘇𝘂𝗿𝗲 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 coming up. Below is a snapshot of actual questions asked in a recent interview 👇 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 & 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴 1. Explain an end-to-end Azure Data Engineering project 2. How do you send notifications in Azure Data Factory? 3. How do you handle a pipeline failure in the middle? 𝗗𝗮𝘁𝗮 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴 & 𝗪𝗮𝗿𝗲𝗵𝗼𝘂𝘀𝗶𝗻𝗴 4. What is SCD Type 2? 5. What is a surrogate key? 6. Star schema vs Snowflake schema 7. What is normalization? 𝗦𝘁𝗼𝗿𝗮𝗴𝗲 & 𝗙𝗶𝗹𝗲 𝗙𝗼𝗿𝗺𝗮𝘁𝘀 8. Parquet vs Delta Lake and what do ACID transactions mean? 9. Delta architecture and Time Travel feature, how it works and how to implement it 10. ADLS Gen1 vs ADLS Gen2 11. Blob Storage vs ADLS Gen2 𝗣𝘆𝗦𝗽𝗮𝗿𝗸 12. Explain every component in Spark architecture in detail 13. What is the explode function in PySpark? 𝗦𝗤𝗟 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀 14. What are OFFSET and LIMIT? 15. How do you find the 2nd highest salary in SQL? 𝗣𝘆𝗦𝗽𝗮𝗿𝗸 / 𝗗𝗮𝘁𝗮𝗯𝗿𝗶𝗰𝗸𝘀 𝗖𝗼𝗱𝗶𝗻𝗴 16. How do you find the 2nd highest salary in PySpark? 17. How do you implement a broadcast join in PySpark? 18. How do you implement SCD Type 2 using PySpark? 19. Write PySpark code to process data from the Bronze layer and load it into the Silver layer 𝗪𝗵𝘆 𝘁𝗵𝗲𝘀𝗲 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝗺𝗮𝘁𝘁𝗲𝗿 1. Focus on end-to-end ownership, not just theory 2. Strong on Databricks + Delta Lake + ADF 3. Covers real-world failure handling & optimization 4. Exactly aligned with 3–5 years Azure Data Engineer expectations #AzureDataEngineer #TigerAnalytics #Databricks #AzureDataFactory #PySpark DeltaLake BigData DataEngineering Data RecentInterview SQL ADF ADB InterviewPreparation

🚨 THESE are the SQL questions that separate the juniors from the seniors. 🚨 ​You can study syntax all day, but when an interviewer drops question #17 on you, can you code it under pressure? ​Don’t be the 90% who freeze. This list is your blueprint. ​I've compiled the most frequent, job-critical SQL queries from dozens of actual FAANG and top tech interviews. Master this list, and you aren’t just ready for the interview—you’re ready for the job. ​👇 YOUR CHALLENGE: 👇 ​Drop your solution to Question #15 (Calculating the running total) in the comments below. Let's build a massive resource together. Let's see who can write the cleanest, most efficient query! ​Share this with someone who needs this for job. 🚀 ​[SQL interview questions, data science interview, data analyst technical questions, FAANG SQL, advanced SQL queries, window functions SQL, joins vs CTEs, relational database, database normalization, query optimization, data engineering prep, coding challenge, tech interview, tech skills, structured query language, database fundamentals, data professional, career in data, interview prep, problem-solving SQL] ​#SQL #InterviewPrep #DataScience DataAnalytics DataEngineering

Part 1- frontend interview questions for startups and product based companies link- https://docs.google.com/document/d/1xVhFljZn5CNXVHC2hQ9wSyEZNIPX9m0aCaGdnccQYOU/edit?usp=drivesdk #interview #frontend #tech #technology #reels #reelsındia #instagram #uber #softwaredevelopment #javascript #javascripts #interviewskills #faang #faangprep

Interview Series Day 02 Perfect for beginners + interview preparation. Follow @coding.bytes1 for daily Java | SQL | DSA content 🚀 #coding #interview #sqlinterview #jobsearch #DataAnalyst

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Interview rounds focused on:📌 ✅SQL (Window functions, Joins, Aggregations) ✅PySpark (Transformations, Broadcast joins, Optimizations) ✅Data Modeling & ETL ✅SCD Type 1 & Type 2 ✅Azure (ADF vs Databricks) ✅Real-world incremental load scenarios ✅Performance tuning discussions #dataprofesional #DataEngineer #corporatelife #hyderabad