Madurai Kamaraj University Data Scientist Interview Questions and Answers

Q1. About your project and experience
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Core Python questions were asked

Q1. Firstly discuss with HR about the job role and confirmed of technical round interview schedule then I did receive the date and time
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Q1. In the technical round will meet with the technical person of the data scientist and I gave the interview starting was very good going but I have stuck in some questions then I didn't select in Infosys
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Q1. Regularization ,when to select l1 and l2
Ans. 
L1 regularization is used for feature selection and L2 regularization is used for preventing overfitting.
• Use L1 regularization when you want to perform feature selection as it tends to produce sparse feature vectors.

• Use L2 regularization when you want to prevent overfitting by penalizing large weights.

• A combination of L1 and L2 regularization (Elastic Net) can be used for a balance between feature selection and prevent
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Q1. Identify geometric alorithm pattern
Ans. 
Geometric algorithm patterns involve solving problems related to geometric shapes and structures.
• Identifying and solving problems related to points, lines, angles, and shapes

• Utilizing geometric formulas and theorems to find solutions

• Examples include calculating area, perimeter, angles, and distances in geometric figures
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Q2. If minimal data, which would you train for categorical prediction model?
Ans. 
I would train a decision tree model as it can handle categorical data well with minimal data.
• Decision tree models are suitable for categorical prediction with minimal data

• They can handle both numerical and categorical data

• Decision trees are easy to interpret and visualize

• Examples: predicting customer churn, classifying spam emails
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Q1. Deployment of RAG
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RAG (Retrieval-Augmented Generation) deployment enhances AI models by integrating external data sources for improved responses.
• Integrate RAG with existing NLP models to enhance context understanding.

• Utilize APIs to fetch real-time data, improving response accuracy.

• Example: Using RAG in customer support to pull relevant FAQs from a database.

• Implement caching mechanisms to optimize retrieval speed.

• Monitor and evaluate mo...read more
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Q2. Building of RAG
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RAG (Red, Amber, Green) is a visual tool for assessing project status and risk levels.
• RAG status indicates project health: Red = critical issues, Amber = potential risks, Green = on track.

• Example: A project with budget overruns may be marked Red.

• RAG can be used in dashboards for quick visual assessments.

• Regular updates to RAG status help in proactive risk management.
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Q1. What is L1 and L2 Regularization?
Ans. 
L1 and L2 regularization are techniques used in machine learning to prevent overfitting by adding penalty terms to the cost function.
• L1 regularization adds the absolute values of the coefficients as penalty term to the cost function.

• L2 regularization adds the squared values of the coefficients as penalty term to the cost function.

• L1 regularization can lead to sparse models by forcing some coefficients to be exactly zer...read more
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Q1. What's the difference between k means and knn
Ans. 
K-means is a clustering algorithm while KNN is a classification algorithm.
• K-means is unsupervised learning, KNN is supervised learning

• K-means partitions data into K clusters based on distance, KNN classifies data points based on similarity to K neighbors

• K-means requires specifying the number of clusters (K), KNN requires specifying the number of neighbors (K)

• Example: K-means can be used to group customers based on purc...read more
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