Kyndryl Data Science Interview Questions and Answers

In the dynamic landscape of data science and analytics, securing a position at a prestigious company like Kyndryl requires not only technical expertise but also a deep understanding of the industry’s intricacies. To help aspiring candidates prepare for their interviews, we’ve compiled a comprehensive list of common questions along with detailed answers that might be asked during the hiring process at Kyndryl.

Technical Interview Questions

Question: Difference in Random Forest and Decision Tree.

Answer: Random Forest is an ensemble learning method that builds multiple decision trees and combines their predictions to improve accuracy and reduce overfitting. Decision Tree is a single tree-based model that recursively splits the data into subsets based on features to make predictions, but it’s more prone to overfitting compared to Random Forest due to its high variance nature.

Question: Difference between Power BI and Tableau?

Answer:

Cost and Licensing:

• Power BI: Offers flexible pricing plans, including a free version and paid options with varied licensing.
• Tableau: Generally pricier, with subscription-based and perpetual licensing models.

Ease of Use:

• Power BI: Known for its user-friendly interface and seamless Microsoft integration.
• Tableau: Offers powerful visualizations but may have a steeper learning curve.

Data Connectivity:

• Power BI: Supports diverse data sources, including Microsoft products and various databases.
• Tableau: Also supports a wide range of data connectors, including databases, cloud services, and web connectors.

Question: Differences in Linear and Logistic Regression.

Answer: Linear regression predicts continuous numeric outcomes, assuming a linear relationship between independent and dependent variables. It’s used for regression tasks where the dependent variable is continuous. Logistic regression, on the other hand, predicts binary categorical outcomes, modeling the log odds of the probability of a binary outcome as a linear combination of predictor variables. It’s suitable for classification tasks where the dependent variable is binary.

Question: Limitations of Power BI and Tableau.

Answer:

Power BI:

• Limited customization options compared to Tableau, especially for complex visualizations and advanced analytics.
• Dependency on the Microsoft ecosystem may limit compatibility with non-Microsoft data sources.
• Performance issues can arise with large datasets or complex queries, impacting responsiveness and scalability.

Tableau:

• Higher cost, especially for advanced features or large-scale deployments.
• Steeper learning curve due to its complex interface and extensive customization options.
• Limited native integration with certain data sources or cloud services, requiring additional connectors for full compatibility.

Question: What are the types of regression models?

Answer:

• Linear Regression: Predicts continuous numeric outcomes by fitting a straight line to the data.
• Logistic Regression: Predicts binary categorical outcomes by modeling the probability of occurrence of an event.
• Polynomial Regression: Fits a polynomial curve to the data to capture non-linear relationships between variables.

Question: What is ETL and what are the types or examples of ETL tools?

Answer: ETL stands for Extract, Transform, Load, a process used to collect data from various sources, transform it into a usable format, and load it into a destination database or data warehouse. Examples of ETL tools include:

• Informatica: A widely used enterprise ETL tool offering features for data integration, data quality, and data governance.
• Talend: An open-source ETL tool with a comprehensive suite of data integration and management capabilities.
• Microsoft SSIS (SQL Server Integration Services): A part of the Microsoft SQL Server suite, offering ETL functionality for data warehousing and business intelligence solutions.

Pandas, Scikit-Learn, and Matplotlib Interview Questions

Question: What is Pandas, and why is it used in data analysis?

Answer: Pandas is an open-source Python library used for data manipulation and analysis. It provides data structures like DataFrame and Series, which allow users to easily work with structured data, and perform data cleaning, filtering, aggregation, and transformation tasks efficiently.

Question: How do you handle missing data in a DataFrame using Pandas?

Answer: Pandas provides several methods for handling missing data, including:

• Using dropna(): Drops rows or columns with missing values.
• Using fillna(): Fills missing values with a specified value or method (e.g., mean, median, forward fill).
• Using interpolate(): Interpolates missing values based on the surrounding data points.

Question: What is the difference between loc and iloc in Pandas?

Answer: loc is used for label-based indexing, where you specify row and column labels to select data. iloc is used for integer-based indexing, where you specify row and column indices to select data. For example, df.loc[2, ‘column’] selects the value at row index 2 and column ‘column’, while df.iloc[2, 3] selects the value at row index 2 and column index 3.

Question: What is Scikit-Learn, and what are its main features?

Answer: Scikit-Learn is a machine-learning library for Python that provides simple and efficient tools for data mining and analysis. Its main features include various supervised and unsupervised learning algorithms, model evaluation and selection tools, data preprocessing techniques, and support for model deployment.

Question: Explain the difference between fit(), transform(), and predict() methods in Scikit-Learn.

Answer:

• fit(): Used to train the model on the training data by learning the parameters from the data.
• transform(): Used to apply transformations to the data, such as feature scaling or dimensionality reduction.
• predict(): Used to make predictions on new data based on the learned parameters from the training data.

Question: What is cross-validation, and why is it important in machine learning?

Answer: Cross-validation is a technique used to assess the performance of a machine learning model by splitting the data into multiple subsets (folds) and training the model on different combinations of training and validation sets. It helps in estimating the model’s performance on unseen data and reduces the risk of overfitting by providing more reliable evaluation metrics.

Question: What is Matplotlib, and what are its main components?

Answer: Matplotlib is a Python library used for creating static, interactive, and animated visualizations. Its main components include:

• pyplot module: Provides a MATLAB-like interface for creating and customizing plots.
• Figure and Axes: Represent the figure and subplots within the figure, where data is plotted.

Question: How do you create different types of plots using Matplotlib?

Answer: Matplotlib supports various types of plots, including line plots, bar plots, scatter plots, histograms, and more. You can create these plots using functions like plot(), bar(), scatter(), hist(), etc., and customize them using parameters such as color, marker style, labels, titles, etc.

Question: What is the difference between plt.show() and plt.savefig() in Matplotlib?

Answer: plt.show() is used to display the plot interactively in the Python environment, while plt.savefig() is used to save the plot as an image file (e.g., PNG, PDF, SVG) without displaying it. plt.show() is typically used for interactive exploration and debugging, while plt.savefig() is used for saving plots for publication or sharing purposes.

Power BI Interview Questions

Question: What is Power BI, and how does it differ from other BI tools?

Answer: Power BI is a business analytics tool developed by Microsoft that allows users to visualize and analyze data from various sources. It stands out from other BI tools due to its integration with other Microsoft products like Excel, Azure, and SQL Server, as well as its user-friendly interface and robust visualization capabilities.

Question: How do you connect to data sources in Power BI?

Answer: In Power BI, you can connect to a wide range of data sources, including databases, files, online services, and streaming data. You can connect to data sources using built-in connectors or custom connectors, import data into Power BI Desktop, and then transform and visualize the data using Power BI tools.

Question: Explain the difference between calculated columns and measures in Power BI.

Answer:

• Calculated columns: Calculated columns are computed columns that are added to a table based on a formula. They are calculated row by row and can be used for filtering, sorting, and aggregating data within the table.
• Measures: Measures are calculations performed on the fly at query time and are not stored in the underlying data model. They are typically used for aggregating data across multiple tables or for creating dynamic calculations based on user interactions.

Question: How do you create relationships between tables in Power BI?

Answer: To create relationships between tables in Power BI, you can use the Manage Relationships dialog in Power BI Desktop. You specify the columns in each table that form the relationship, and Power BI automatically detects and creates the relationship based on matching values in those columns. Relationships are essential for enabling cross-filtering and slicing across multiple tables in a report.

Question: What is Power Query in Power BI, and how is it used?

Answer: Power Query is a data transformation and data preparation tool in Power BI that allows users to connect, transform, and clean data from various sources before loading it into Power BI Desktop. With Power Query, users can perform tasks such as filtering, sorting, merging, and shaping data to meet their analysis needs.

Question: How do you schedule data refresh in Power BI Service?

Answer: To schedule data refresh in the Power BI Service, you need to publish your Power BI report to the Power BI Service and configure a data source for refresh. You can then set up a refresh schedule in the dataset settings, specifying the frequency and time of day for the refresh to occur. Power BI Service will automatically refresh the data according to the schedule you define.

Tableau Interview Questions

Question: What is Tableau, and why is it used in data visualization?

Answer: Tableau is a powerful data visualization tool that allows users to create interactive and insightful visualizations from various data sources. It is widely used for data exploration, analysis, and presentation, enabling users to gain actionable insights and make data-driven decisions.

Question: How do you connect to data sources in Tableau?

Answer: In Tableau, you can connect to a wide range of data sources, including databases, files, online services, and cloud platforms. You can connect to data sources using built-in connectors or custom connectors, import data into Tableau Desktop, and then create visualizations and dashboards using the imported data.

Question: What are the dimensions and measures in Tableau?

Answer:

• Dimensions: Dimensions are categorical data fields that provide context and describe the characteristics of data. They are typically used for grouping, segmenting, and filtering data in Tableau visualizations.
• Measures: Measures are numeric data fields that represent quantitative values and can be aggregated or computed. They are typically used for performing calculations, creating metrics, and generating insights in Tableau visualizations.

Question: Explain the difference between discrete and continuous fields in Tableau.

Answer:

• Discrete fields: Discrete fields contain distinct, separate values that are used for categorical data. They are represented by blue pills in Tableau and are typically used for grouping and labeling data in visualizations.
• Continuous fields: Continuous fields contain continuous numeric values that are used for quantitative data. They are represented by green pills in Tableau and are typically used for creating axes, scales, and reference lines in visualizations.

Question: How do you create calculated fields in Tableau?

Answer: To create calculated fields in Tableau, you can use the calculated field editor within Tableau Desktop. You can define calculations using Tableau’s formula language, which includes mathematical operators, functions, and logical expressions. Calculated fields allow users to perform custom calculations, transformations, and aggregations on the data.

Question: What is a Tableau dashboard, and how do you create one?

Answer: A Tableau dashboard is a collection of visualizations, worksheets, and other elements arranged on a single canvas to provide a comprehensive view of the data. To create a dashboard in Tableau, you can drag and drop visualizations and worksheets onto the dashboard canvas, arrange them as desired, and then customize the layout, formatting, and interactivity to create an engaging and informative dashboard.

Question: How do you publish Tableau visualizations and dashboards to the Tableau Server?

Answer: To publish Tableau visualizations and dashboards to the Tableau Server, you can use the publish feature within Tableau Desktop. You specify the server connection details, project, and permissions, and then publish the workbook to the Tableau Server. Once published, users with appropriate access rights can view and interact with the visualizations and dashboards through a web browser.

SQL Interview Questions

Question: What is SQL, and why is it important in data management?

Answer: SQL (Structured Query Language) is a standard programming language used for managing and manipulating relational databases. It is important in data management because it allows users to perform tasks such as querying data, inserting, updating, and deleting records, creating and modifying database schema, and performing data analysis and reporting.

Question: What is the difference between GROUP BY and ORDER BY in SQL?

Answer:

• GROUP BY: Groups rows that have the same values into summary rows, typically to apply aggregate functions like COUNT, SUM, AVG, etc. It is used in conjunction with aggregate functions to perform operations on groups of data.
• ORDER BY: Sorts the result set in ascending or descending order based on specified columns. It is used to sort the rows returned by a query, but it does not perform any grouping or aggregation.

Question: What is a subquery in SQL, and how is it used?

Answer: A subquery is a query nested inside another query and enclosed within parentheses. It is used to return data that will be used as a condition or value in the main query. Subqueries can be used in SELECT, INSERT, UPDATE, or DELETE statements to filter, join, or manipulate data based on the results of another query.

Question: What is a primary key, and why is it important in database design?

Answer: A primary key is a unique identifier for each record in a table. It ensures that each row in the table is uniquely identified and provides a way to enforce entity integrity. Primary keys are essential in database design because they help maintain data integrity, enforce constraints, and facilitate efficient data retrieval through indexing.

Question: What is an SQL view, and how is it used?

Answer: A SQL view is a virtual table that contains data derived from one or more tables. It is defined by a query and behaves like a regular table in many ways. Views are used to simplify complex queries, encapsulate frequently used logic, provide security by limiting access to specific columns or rows, and present a consistent view of the data to users.

Conclusion

Preparing for a data science and analytics interview at Kyndryl requires a combination of technical expertise, domain knowledge, and problem-solving skills. By familiarizing yourself with these common questions and practicing your responses, you’ll be better equipped to showcase your abilities and secure your dream job in this exciting field at Kyndryl. Good luck!