Razorpay Data Engineer II Interview Questions and Answers

Sql, python, statistical questions

Q1. How daily work looks like?
Ans. 
Daily work involves analyzing data, creating reports, and communicating findings to stakeholders.
• Reviewing and cleaning data sets

• Creating visualizations and dashboards

• Conducting statistical analysis

• Communicating insights to non-technical stakeholders
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Q2. Most ise tools?
Ans. 
Some of the most commonly used tools for data analysis include Excel, Python, R, Tableau, and SQL.
• Excel is widely used for data manipulation and analysis.

• Python and R are popular programming languages for data analysis and machine learning.

• Tableau is a powerful data visualization tool.

• SQL is essential for querying databases and extracting data.
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4 sql tasks on 3 tables. Joins, subqueries

Cancellation in the number of bookings in an app has increased exponentially in the past few weeks, How would you conduct an RCA on the same.

Medium level questions

Q1. Sorting Python question
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Q2. Hard SQL question
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Q1. DBSCAN Clustering
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Q2. How does current work helps solving use cases for PayU
Ans. 
My current work involves analyzing transaction data to identify patterns and trends, which can help PayU optimize their payment processing services.
• Analyzing transaction data to identify fraudulent activities and improve security measures for PayU

• Developing predictive models to forecast transaction volumes and optimize payment processing times

• Utilizing machine learning algorithms to personalize user experiences and inc...read more
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Q1. SQL coding tests
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Q1. Discussed about hybrid mode of work
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Q1. Projects Mentioned in Resume
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Q2. Related to Python, Pyspark
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We were trained for 2 weeks on web dev. and data science and were given individual 2 projects, according to the performance on them and a final interview, candidates were selected.

Q1. All about the assignment done
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Q2. Your interest and particular domain you want to work in
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Q1. Explain the project architecture.
Ans. 
The project architecture consists of multiple components that work together to process and analyze data.
• The architecture follows a distributed computing model.

• Data is collected from various sources and stored in a data lake or data warehouse.

• Data processing and transformation tasks are performed using tools like Apache Spark or Hadoop.

• Processed data is then loaded into a database or data store for further analysis.

• Anal...read more
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Q2. What are flaws in your architecture?
Ans. 
Our architecture lacks scalability and real-time processing capabilities.
• Our current architecture is not designed to handle large volumes of data.

• Real-time processing of data is not possible with our current setup.

• We lack a proper data pipeline for efficient data processing.

• Our architecture is not fault-tolerant and lacks redundancy.

• We need to improve our data storage and retrieval mechanisms.

• Our architecture does not ...read more
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