Are you gearing up for a data science and analytics interview at Bajaj Finance? As one of the leading financial services companies, Bajaj Finance relies heavily on data-driven insights to make informed decisions. To help you ace your interview, here are some common questions and their answers that you might encounter:
Table of Contents
Technical Interview Questions
Question: What are transactions in DBMS?
Answer: In a DBMS (Database Management System), transactions are sets of operations that are performed together as a single unit of work. These operations might include adding, updating, or deleting data from the database. Transactions ensure that all the operations either are completed successfully or none of them are applied to maintain the integrity and consistency of the database. For example, when you transfer money from one account to another online, that whole process is a transaction. If any part fails (like insufficient funds), the entire transaction is rolled back, so no money is moved.
Question: What are ACID properties?
Answer: ACID properties ensure that transactions in a database are:
- Atomic: Either all changes in a transaction are applied, or none.
- Consistent: Transactions maintain the database rules and integrity constraints.
- Isolated: Multiple transactions can run concurrently without interfering with each other.
- Durable: Committed changes are permanent, surviving system failures.
Question: What is Py Spark?
Answer: PySpark is a Python library that enables interaction with Apache Spark, a powerful open-source framework for big data processing. It allows data scientists and analysts to write Spark applications using Python, making it easier to work with large-scale data processing tasks. PySpark provides APIs for various operations such as data manipulation, SQL queries, machine learning, and streaming processing on distributed datasets across clusters.
Question: What is Hadoop?
Answer: Hadoop is an open-source framework for storing and processing big data across clusters. It includes HDFS for distributed storage and MapReduce for parallel data processing. It’s used to handle large datasets and run applications on clusters of commodity hardware.
Question: What is DFS?
Answer: DFS, or Distributed File System, allows files and folders to be stored and accessed across multiple networked computers. It provides a unified view of data, even though it’s physically distributed across different machines. DFS helps manage large data efficiently and ensures fault tolerance by replicating data across nodes in the network.
Question: Explain the Difference between merge and quick sort.
Answer:
Merge Sort:
- Divides array into halves, sorts each, then merges.
- Stable sort, preserving the order of equal elements.
- Always O(n log n) time, but needs extra space for merging.
Quick Sort:
- Chooses pivot, partitions array, and sorts sub-arrays.
- Generally faster due to in-place sorting.
- Can degrade to O(n^2) in worst cases with poor pivots.
Question: What is MongoDB?
Answer: MongoDB is a popular open-source NoSQL database that uses a document-oriented data model. It stores data in flexible, JSON-like documents, making it easy to work with structured and unstructured data. MongoDB is designed for scalability and high performance, allowing for horizontal scaling across multiple servers. It is commonly used for applications requiring flexible schemas, fast development, and handling large volumes of data.
Question: What is a Graph?
Answer: In data structures, a graph is a collection of nodes (vertices) linked by edges. It represents connections or relationships between entities, like social networks or routes. Graphs can be directed (one-way edges), undirected (two-way edges), or weighted (edges with values like distances). They’re used to model complex networks and are stored and queried efficiently in graph databases like Neo4j.
Question: What is DFS?
Answer: DFS stands for Depth-First Search. It’s an algorithm used to traverse or search through a graph or tree data structure. In DFS, the algorithm starts at the root node (or any arbitrary node) and explores as far as possible along each branch before backtracking. This means it goes deep into the structure before exploring other branches. It’s often used to solve problems involving connected components, cycle detection, and path finding in graphs.
Question: Explain Linear Regression and Random Forest algorithm.
Answer:
Linear Regression: Linear regression is a statistical method for modeling the relationship between a dependent variable and one or more independent variables. It seeks to find the best-fit line that represents how changes in the independent variables influence the dependent variable. The model’s equation, y = mx + b, provides insights into these relationships, where ‘y’ is the predicted value, ‘x’ is the input feature, ‘m’ is the slope, and ‘b’ is the intercept.
Random Forest: Random Forest is an ensemble learning technique that constructs multiple decision trees during training. These trees are then combined to make more accurate and stable predictions. Each tree in the Random Forest is trained on a random subset of the data and a random subset of features.
Question: Explain Normal distribution.
Answer: Normal Distribution: The Normal distribution, also known as the Gaussian distribution, is a symmetric, bell-shaped probability distribution. It is characterized by its mean (average) and standard deviation. In a Normal distribution, the data clusters around the mean, with most values falling close to the center and fewer values farther away in a predictable pattern.
Question: What is Standard Deviation and Variance?
Answer:
- Standard Deviation: Standard deviation measures the spread of data points from the mean, showing how much they deviate from the average. A low standard deviation indicates data points are close to the mean, while a high one suggests they are spread out. It’s symbolized by ‘σ’ (sigma) and is the square root of the variance.
- Variance: Variance quantifies the average of the squared differences of data points from the mean. It’s a measure of how much the data points vary from the average. A higher variance means more spread, while a lower variance indicates data points are closer to the mean. It’s denoted by ‘σ²’ (sigma squared) and is the square of the standard deviation.
Question: What are Joins in SQL?
Answer: Joins in SQL:
- Inner Join: Retrieves rows with matching values in both tables based on a specified condition.
- Left Join: Gets all rows from the left table and matching rows from the right table; unmatched rows have NULL values.
- Right Join: Retrieves all rows from the right table and matching rows from the left table; unmatched rows have NULL values.
- Full Join: Gets all rows when there is a match in either table; unmatched rows have NULL values for the non-matching columns.
Question: What is Aggregation?
Answer: Aggregation in SQL involves the process of collecting and summarizing data from multiple rows into a single result. It allows you to perform calculations on a set of values to produce summary statistics. Common SQL aggregate functions include:
- SUM: Calculates the sum of values in a column.
- AVG: Computes the average (mean) of values in a column.
- COUNT: Counts the number of rows in a table or the number of non-null values in a column.
- MIN: Finds the minimum value in a column.
- MAX: Retrieves the maximum value in a column.
Question: Explain the Having clause.
Answer: The HAVING clause in SQL is used with the GROUP BY clause to filter groups of rows based on a specified condition. It is specifically designed for filtering aggregated data, allowing you to apply conditions to the results of aggregate functions like SUM, AVG, COUNT, etc. This clause comes after the GROUP BY and is used to restrict the groups returned by the query based on the condition specified.
DBMS Interview Questions
Question: Explain the difference between DELETE and TRUNCATE commands.
Answer: DELETE is a DML (Data Manipulation Language) command used to remove rows from a table based on a condition. It keeps the structure of the table intact but removes the data. TRUNCATE is a DDL (Data Definition Language) command that removes all rows from a table. It also resets identity columns and releases allocated memory, but it cannot be rolled back.
Question: What are the primary key and foreign key in a database?
Answer: A primary key is a unique identifier for each record in a table. It ensures that each row can be uniquely identified. A foreign key, on the other hand, establishes a relationship between two tables. It is a field in one table that references the primary key in another table, enforcing referential integrity.
Question: Explain the concept of normalization in databases.
Answer: Normalization is the process of organizing data in a database to reduce redundancy and improve data integrity. It involves breaking down a table into smaller, related tables to eliminate duplicate data and ensure that each piece of information is stored in only one place. The goal of normalization is to prevent data anomalies and make the database structure more efficient.
Question: What is the difference between OLTP and OLAP?
Answer: OLTP (Online Transaction Processing) is a type of system that manages transaction-oriented applications, such as order processing and online banking. It is optimized for handling a large number of short, fast transactions. OLAP (Online Analytical Processing), on the other hand, is used for data analysis and reporting. It involves complex queries on large volumes of historical data to gain insights and make strategic decisions.
Question: Explain the concept of a view in a database.
Answer: A view is a virtual table in a database that is based on the result of a SELECT query. It does not store any data itself but provides a way to present data from one or more tables in a predefined format. Views can be used to simplify complex queries, restrict access to specific columns or rows of a table, and provide a level of abstraction between the user and the underlying tables.
Oops Interview Questions
Question: What is Object-Oriented Programming (OOP)?
Answer: Object-oriented programming is a programming paradigm based on the concept of “objects,” which can contain data (attributes) and code (methods). It allows for the creation of modular, reusable, and maintainable code by organizing data and behavior into self-contained units.
Question: Explain the four pillars of OOP.
Answer: The four pillars of OOP are:
- Encapsulation: Bundling data (attributes) and methods (behavior) together within an object, hiding the internal state of an object and only allowing access through defined interfaces.
- Inheritance: Allowing a class (subclass) to inherit properties and behavior from another class (superclass), facilitating code reuse, and creating a hierarchy of classes.
- Polymorphism: The ability of objects to take on multiple forms or behaviors depending on the context. It allows different classes to be treated as instances of the same class through a common interface.
- Abstraction: Simplifying complex systems by hiding unnecessary implementation details and showing only the essential features of an object.
Question: What is the difference between a class and an object?
Answer: A class is a blueprint or template for creating objects. It defines the properties (attributes) and behaviors (methods) that objects of that class will have. An object, on the other hand, is an instance of a class, representing a specific entity with its unique state and behavior.
Question: Explain the concept of inheritance in OOP with an example.
Answer: Inheritance allows a class (subclass) to inherit properties and behaviors from another class (superclass). For example, consider a superclass “Vehicle” with properties like “speed” and “fuelType,” and methods like “accelerate()” and “brake().” We can then create a subclass “Car” that inherits from “Vehicle” and adds its specific properties like “brand” and methods like “startEngine().”
Question: What is method overriding in OOP?
Answer: Method overriding occurs when a subclass provides a specific implementation for a method that is already defined in its superclass. This allows the subclass to provide its behavior for the method, which will be used when the method is called on objects of the subclass.
Question: What is the difference between an abstract class and an interface?
Answer: An abstract class is a class that cannot be instantiated on its own and may contain both abstract (unimplemented) and concrete (implemented) methods. It provides a partial implementation and is used when you want to define some common behavior that subclasses can inherit. An interface, on the other hand, is a collection of abstract methods that define a contract for classes to implement. A class can implement multiple interfaces, but can only inherit from one class.
Technical Interview Topics
- SQL questions
- DBMS-related questions
- Python,
- Machine Learning
- Cloud Computing
- OOPs concepts
- NLP questions
- Conclusion
Conclusion
Preparing for a data science and analytics interview at Bajaj Finance requires a solid understanding of fundamental concepts, practical experience with tools and techniques, and the ability to articulate your knowledge effectively. These questions and answers should serve as a valuable guide to help you succeed in your interview and showcase your expertise in the field of data science and analytics. Good luck!
