Meesho PySpark Interview Questions for Data Engineers in 2025 Preparing for a PySpark interview? Let’s tackle some commonly asked questions, along with practical answers and insights to ace your next Data Engineering interview at Meesho or any top-tier tech company. 1. Explain how caching and persistence work in PySpark. When would you use cache() versus persist() and what are their performance implications? Answer : Caching : Stores data in memory (default) for faster retrieval. Use cache() when you need to reuse a DataFrame or RDD multiple times in a session without specifying storage levels. Example: python df.cache() df.count() # Triggers caching Persistence : Allows you to specify storage levels (e.g., memory, disk, or a combination). Use persist() when memory is limited, and you want a fallback to disk storage. Example: python from pyspark import StorageLevel df.persist(StorageLevel.MEMORY_AND_DISK) df.count() # Triggers persistence Performance Implications : cache() is ...
Deloitte Data Analyst Interview Questions: Insights and My Personal Approach to Answering Them 1. Tell us about yourself and your current job responsibilities. Example Answer: "I am currently working as a Data Analyst at [Company Name], where I manage and analyze large datasets to drive business insights. My responsibilities include creating and maintaining Power BI dashboards, performing advanced SQL queries to extract and transform data, and collaborating with cross-functional teams to improve data-driven decision-making. Recently, I worked on a project where I streamlined reporting processes using DAX measures and optimized SQL queries, reducing report generation time by 30%." 2. Can you share some challenges you encountered in your recent project involving Power BI dashboards, and how did you resolve them? Example Challenge: In a recent project, one of the key challenges was handling complex relationships between multiple datasets, which caused performance issues and in...