Quant Systems India Big Data Engineer Interview Questions and Answers

Easy ques to answer basic

Easy ques to write. not tough ques

Q1. Easy ques to write.Not tough
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Q2. Easy Ques to write not tough
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Q1. Great to work here in Bajaj
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Q2. 15min interview and basic of SQL
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Q1. Get the maximum salary of the employee from the employee and department table
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Q2. Explain the data analysis process that you would follow for employee salary table
Ans. 
The data analysis process for an employee salary table involves data cleaning, exploration, visualization, and modeling.
• Start by cleaning the data to remove any duplicates, missing values, or outliers.

• Explore the data by calculating basic statistics like mean, median, and standard deviation.

• Visualize the data using histograms, box plots, or scatter plots to identify patterns or trends.

• Perform statistical modeling to an...read more
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Q3. Explain the challenges you faced in your project
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I faced challenges in managing large datasets and ensuring data accuracy in my project.
• Managing large volumes of data from multiple sources

• Ensuring data accuracy and consistency

• Dealing with missing or incomplete data

• Handling data cleaning and preprocessing tasks

• Addressing technical issues with data storage or processing tools
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It was moderate and also easy to crack

Q1. Write a SQL queries
Ans. 
SQL queries to retrieve data from a database
• Use SELECT statement to retrieve data from tables

• Use WHERE clause to filter results based on specific conditions

• Use JOIN clause to combine data from multiple tables
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Q2. Use case when while writing a query
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Using a case statement in a query to handle different conditions
• Use CASE statement to handle different conditions in a query

• CASE can be used for conditional logic within SELECT, WHERE, ORDER BY clauses

• Example: SELECT column1, CASE WHEN condition1 THEN 'Result1' ELSE 'Result2' END AS new_column FROM table_name
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Q1. Explain Data types in SQL?
Ans. 
Data types in SQL define the type of data that can be stored in a column of a table.
• Data types include integer, float, text, date, boolean, etc.

• Each data type has specific properties and constraints.

• Examples: INT for integers, VARCHAR for variable-length character strings.
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Q2. Explain the difference between DBMS and RDBMS?
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DBMS is a software system that manages databases, while RDBMS is a type of DBMS that stores data in a structured format using tables.
• DBMS stands for Database Management System, which is a software system that allows users to interact with a database.

• RDBMS stands for Relational Database Management System, which is a type of DBMS that stores data in a structured format using tables with relationships between them.

• RDBMS e...read more
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English, quant, logical reasoning

Q1. Wacc, coc, beta
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Q2. Coc, equity, capm
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Q1. What is p value?
Ans. 
A p-value is a measure used in statistical hypothesis testing to determine the strength of evidence against the null hypothesis.
• A p-value is the probability of obtaining results as extreme as the observed results, assuming the null hypothesis is true.

• A p-value is compared to a significance level (usually 0.05) to determine if the null hypothesis should be rejected.

• A p-value less than the significance level indicates st
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Q2. Tell the output a**2
Ans. 
The output of a**2 is the square of the value of a.
• The output is the value of a multiplied by itself

• For example, if a = 3, then the output would be 9 (3*3)
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Q3. Difference between append() and concat
Ans. 
append() adds elements to a single DataFrame, while concat() combines multiple DataFrames.
• append() is a method used to add rows to a DataFrame.

• concat() is a function used to combine multiple DataFrames along a particular axis.

• append() modifies the original DataFrame, while concat() returns a new DataFrame.

• Example: df1.append(df2) vs pd.concat([df1, df2])
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Q1. Explain error handling in PySpark
Ans. 
Error handling in PySpark involves using try-except blocks and logging to handle exceptions and errors.
• Use try-except blocks to catch and handle exceptions in PySpark code

• Utilize logging to record errors and exceptions for debugging purposes

• Consider using the .option('mode', 'PERMISSIVE') method to handle corrupt records in data processing
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Q1. Data Warehousing related questions
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Q1. Data Modelling related questions
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