Bajaj Finserv Data Scientist 2 Interview Questions and Answers

Q1. Explain about projects completed during academics and role performed?
Ans. 
During my academics, I completed a project on analyzing customer behavior using machine learning techniques.
• Developed a predictive model to forecast customer churn based on historical data

• Used Python for data preprocessing, feature engineering, and model building

• Collaborated with a team to gather requirements and present findings to stakeholders
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Q2. SQL based questions
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Q1. Advanced sql on ctl
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Q2. Python data structure
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Q1. What are joins of sql
Ans. 
Joins in SQL are used to combine rows from two or more tables based on a related column between them.
• Joins are used to retrieve data from multiple tables based on a related column

• Common types of joins include INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN

• Example: SELECT * FROM table1 INNER JOIN table2 ON table1.column = table2.column
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Q2. Explain about self join
Ans. 
Self join is a SQL technique where a table is joined with itself to compare rows within the same table.
• Self join is used when we want to compare rows within the same table.

• It requires aliasing the table with different names to differentiate between the two instances.

• Commonly used in hierarchical data structures or when comparing related records.
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Q1. Explain spark stages and tasks
Ans. 
Spark stages are a collection of tasks that are executed together to perform a specific computation.
• Stages are created based on the transformations and actions in a Spark job.

• Each stage consists of a set of tasks that can be executed in parallel.

• Stages are divided into two types: narrow (one-to-one) and wide (one-to-many) transformations.

• Tasks within a stage are executed on different partitions of the data in parallel.

• ...read more
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Q2. Sql windoq questions
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Q1. Advance delta table optimizatoins
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Q2. Data modelling in previous project
Ans. 
Implemented data modelling using star schema in a retail analytics project.
• Designed star schema to optimize query performance

• Used fact and dimension tables to organize data

• Implemented ETL processes to populate the data warehouse

• Utilized tools like SQL, Python, and Apache Spark for data modelling
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Q1. I was asked to write code and some scenario based question on Big Data etc
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2nd with VP which is easy but he seems not okay with my ECTC

Q1. Questions on pyspark coding, windowing function and all
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Q2. Questions on hive and yarn
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Spark Sql and Spark Scripting

Data Modelling for retail brand like dmart

ETL Pipeline which handles streaming data as well

Pyspark, Hive, Yarn, Python

Q1. Questions on windowing function
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Q2. Questions on spark arch and python
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