10+ Axtria Analyst Interview Questions and Answers

Rotate a given array to the left by K steps.

• Create a new array to store the rotated elements.

• Use modular arithmetic to handle cases where K is greater than the array size.

• Shift elements to the left by K positions and fill in the remaining elements from the original array.

• Return the rotated array.

Trans-shipment problem can be solved in Excel Solver by setting up a linear programming model.

• Define decision variables for the amount of goods to be shipped between each pair of locations.

• Set up constraints to ensure that the supply and demand at each location are met.

• Add constraints to limit the amount of goods that can be shipped through intermediate locations.

• Define the objective function to minimize the total cost of shipping.

• Use Excel Solver to find the optimal solution...read more

The number of parts divided by one circle and four lines is 11.

• Each line intersects with every other line and the circle.

• Each intersection point creates a new part.

• The circle itself also counts as a part.

My area of interest is artificial intelligence and machine learning.

• I am fascinated by the potential of AI and ML to revolutionize various industries.

• I enjoy exploring the latest advancements in deep learning and natural language processing.

• I am particularly interested in the ethical implications of AI and the need for responsible development.

• Some of my favorite examples of AI in action include self-driving cars, virtual assistants, and image recognition technology.

Transportation problem is a linear programming problem that deals with minimizing the cost of transporting goods from sources to destinations.

• It involves determining the optimal way to transport goods from sources to destinations

• It considers factors such as cost, capacity, and demand

• It can be solved using various methods such as the North-West Corner Method, Least Cost Method, and Vogel's Approximation Method

• Example: A company needs to transport goods from three factories to ...read more

Joins in SQL are used to combine rows from two or more tables based on a related column between them.

• Types of joins include INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN.

• INNER JOIN returns rows when there is at least one match in both tables.

• LEFT JOIN returns all rows from the left table and the matched rows from the right table.

• RIGHT JOIN returns all rows from the right table and the matched rows from the left table.

• FULL JOIN returns rows when there is a match in one of t...read more

I did not take the GRE exam.

• I did not apply for a program that required GRE scores.

• My focus was on gaining practical experience through internships and projects.

• I have excelled in my academic and professional pursuits without a GRE score.

CAT was challenging but I managed to do well.

• CAT was a difficult exam but I prepared well for it.

• I had to manage my time effectively during the exam.

• I am confident that I did well in the exam.

• The exam tested my analytical and problem-solving skills.

• Overall, I am satisfied with my performance in CAT.

I approach daily tasks analytically by breaking them down into smaller steps and using data-driven decision making.

• I start by clearly defining the task and its objectives.

• I gather relevant data and information to understand the problem or task at hand.

• I break down the task into smaller, manageable steps or sub-tasks.

• I prioritize the steps based on their importance and urgency.

• I use data analysis techniques to identify patterns, trends, and insights.

• I make data-driven decision...read more

Yes, Object-Oriented Programming (OOP) is a programming paradigm based on the concept of objects, which can contain data and code.

• OOP focuses on creating objects that interact with each other to solve problems

• Key principles of OOP include encapsulation, inheritance, and polymorphism

• Examples of OOP languages include Java, C++, and Python

Multicollinearity can be checked using correlation matrix, variance inflation factor (VIF), and eigenvalues.

• Calculate the correlation matrix between independent variables. High correlation coefficients indicate multicollinearity.

• Calculate the VIF for each independent variable. VIF values above 10 indicate multicollinearity.

• Check for multicollinearity using eigenvalues of the correlation matrix. If there are eigenvalues close to zero, it indicates multicollinearity.

SVM algorithm is a supervised machine learning model used for classification and regression tasks by finding the optimal hyperplane.

• SVM stands for Support Vector Machine

• It works by finding the hyperplane that best separates the classes in the feature space

• It can handle both linear and non-linear data by using different kernels like linear, polynomial, and radial basis function

• SVM aims to maximize the margin between the classes to improve generalization

• It is commonly used in i...read more

Passion for solving complex problems with data-driven insights

• Fascination with the power of data to drive decision-making

• Interest in leveraging statistical analysis and machine learning algorithms

• Desire to make a positive impact through data-driven solutions

SQL is a traditional relational database management system, while NoSQL is a non-relational database system.

• SQL is structured query language used for relational databases

• NoSQL is a non-relational database system that can handle unstructured data

• SQL databases are table-based, NoSQL databases are document, key-value, graph, or wide-column stores

• SQL is good for complex queries and transactions, NoSQL is better for large amounts of data and scalability

R2 score is a statistical measure that represents the proportion of the variance in the dependent variable that is predictable from the independent variable(s).

• R2 score ranges from 0 to 1, with 1 indicating a perfect fit.

• It is often used in regression analysis to evaluate the goodness of fit of a model.

• R2 score can be interpreted as the percentage of the response variable variation that is explained by a linear model.