Real questions from top companies · hard
Describe how you would optimize a join between two large tables where one is significantly smaller, using broadcast joins in PySpark.
Describe how you would optimize slow-running Spark jobs in a distributed environment.
Describe the projects emphasizing Spark, Hadoop, or Azure for large-scale data processing
Describe the role of a DAG Scheduler in PySpark
Describe the stages of a Spark job and strategies to optimize Spark performance for large datasets.
Design an ETL pipeline using Kafka and Spark Streaming
Difference between Presto vs. Spark underlying architecture
Discuss common transformations used in Spark code.
Discuss file formats (Parquet, Avro, ORC) and storage strategies.
Discuss how you integrated Azure services into your Spark application.
Discuss stages and tasks in a Spark execution plan.
Explain Apache Spark fundamentals, OOM scenarios and their resolutions, optimization techniques, strategies for optimized joins, and handling data skewness with Key Salting techniques.
Explain Azure Databricks architecture and its integration with other Azure services.
Explain Delta Live Tables and their features, such as declarative pipeline definition and automatic data validation.
Explain Delta Table features – Z-ordering and Time Travel.
Explain Delta Time Travel and the purpose of the vacuum command.
Explain Hive, its purpose, and its default metadata storage.
Explain MapReduce Architecture.
Explain PySpark's Catalyst Optimizer.
Explain SCD1 and SCD2 in Databricks PySpark with examples.
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