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

Suppose you are tasked with building a RAG application serving thousands of requests per second. What components and design principles would you consider to ensure reliability and scalability?

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

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

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