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𝐁𝐚𝐬𝐞𝐝 𝐨𝐧 𝐦𝐲 𝐞𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞, 𝐡𝐞𝐫𝐞 𝐚𝐫𝐞 𝐭𝐡𝐞 𝐓𝐨𝐩 10 𝐓𝐨𝐮𝐠𝐡𝐞𝐬𝐭 𝐒𝐩𝐚𝐫𝐤 & 𝐒𝐩𝐚𝐫𝐤 𝐃𝐚𝐭𝐚𝐅𝐫𝐚𝐦𝐞 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 𝐭𝐡𝐚𝐭 𝐲𝐨𝐮 𝐜𝐚𝐧 𝐞𝐱𝐩𝐞𝐜𝐭 𝐝𝐮𝐫𝐢𝐧𝐠 𝐲𝐨𝐮𝐫 𝐢𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰

📌 How would you optimize a Spark job that processes billions of records, but it’s running slower than expected? What steps would you take to identify the bottleneck?

📌 Explain the difference between cache() and persist() in Spark. When would you use one over the other in a real-world scenario?

📌 You have a Spark job that performs multiple transformations on a large dataset. How can you minimize the number of stages in your job to improve performance?

📌 Given a Spark DataFrame, how would you optimize a groupBy operation that involves large datasets? Can you reduce the shuffle involved?

📌 What is the impact of partitioning on Spark performance? How would you decide on the number of partitions for a given DataFrame?

📌 You are working with a skewed dataset in Spark, where one partition has significantly more data than others. How would you handle data skew to optimize performance?📌 Explain how Spark’s Catalyst Optimizer works. How does it optimize queries, and how can you tune the Catalyst Optimizer for better performance?

📌 Imagine you’re reading data from an external source (e.g., a Hive table) and performing multiple joins. What optimizations would you implement to speed up the process?

📌 You’re asked to handle an ETL pipeline in Spark where you need to perform transformations on structured data. How would you leverage Spark SQL and DataFrame APIs for efficiency?

📌 You need to perform windowed operations on a large dataset (e.g., moving averages). How would you optimize Spark's execution for these types of operations to avoid expensive shuffles?