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Describe a significant project where you designed and implemented a Spark-based architecture for processing and analyzing large volumes of data. What were the key challenges you faced, and how did you address them?

Suppose you're tasked with designing a fault-tolerant Spark-based data pipeline that processes both batch and streaming data. What components and design principles would you consider to ensure reliability and scalability?

Describe a scenario where you needed to ensure data lineage and traceability in a Spark-based ETL pipeline. How did you approach this requirement?

What are your preferred techniques for monitoring and managing the performance of Spark jobs in a production environment? How do you ensure optimal resource utilization and cost-effectiveness?

Describe a complex big data architecture you've designed that involves data ingestion, storage, processing, and analytics. How did you balance performance, scalability, and maintainability? What services did you utilize and how did they contribute to the success of the project?

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