LTIMindtree Data Science Intern Interview Questions and Answers

Q1. What are Large Language Models?
Ans. 
Large Language Models are advanced AI models that can generate human-like text based on input data.
• Large Language Models use deep learning techniques to understand and generate text.

• Examples include GPT-3 (Generative Pre-trained Transformer 3) and BERT (Bidirectional Encoder Representations from Transformers).

• They are trained on vast amounts of text data to improve their language generation capabilities.
Answered by AI
Add your answer
Q2. Do you know about RAGs?
Ans. 
RAGs stands for Red, Amber, Green. It is a project management tool used to visually indicate the status of tasks or projects.
• RAGs is commonly used in project management to quickly communicate the status of tasks or projects.

• Red typically indicates tasks or projects that are behind schedule or at risk.

• Amber signifies tasks or projects that are on track but may require attention.

• Green represents tasks or projects that ar...read more
Answered by AI
Add your answer
Q3. Which is the best clustering algorithm?
Ans. 
There is no one-size-fits-all answer as the best clustering algorithm depends on the specific dataset and goals.
• The best clustering algorithm depends on the dataset characteristics such as size, dimensionality, and noise level.

• K-means is popular for its simplicity and efficiency, but may not perform well on non-linear data.

• DBSCAN is good for clusters of varying shapes and sizes, but may struggle with high-dimensional d...read more
Answered by AI
Add your answer

Q1. Basic python and machine learning questions
Add your answer

Q1. Salary expectation and working location related questions and why you want to join wipro
Add your answer

This was good aptitude test computer based

Coding round share screen and code

Q1. Explains OOPs concept
Add your answer
Q2. Explain SOLID principles
Add your answer

Q1. Tell me about your self?
Add your answer
Q2. What is maths and stats
Ans. 
Maths and stats refer to the study of mathematical concepts and statistical methods for analyzing data.
• Maths involves the study of numbers, quantities, shapes, and patterns.

• Stats involves collecting, analyzing, interpreting, and presenting data.

• Maths is used to solve equations, calculate probabilities, and model real-world phenomena.

• Stats is used to make informed decisions, draw conclusions, and test hypotheses.

• Both ma...read more
Answered by AI
Add your answer

Confusion matrix what are your job rolls explain me Gradient boosting algorithm?

60 min hackerrank test,with one mysql medium difficulty question and one python medium/little hard level difficulty

Q1. Technical covering each topic from stats, python, ml, dl, nlp,project
Add your answer

Q1. Project-related in-depth discussion, few case scenarios, stats question
Add your answer

Q1. Difference between supervised and unsupervised learning
Ans. 
Supervised learning uses labeled data to train a model, while unsupervised learning uses unlabeled data.
• Supervised learning requires a target variable for training the model.

• Examples of supervised learning include classification and regression.

• Unsupervised learning finds patterns and relationships in data without a target variable.

• Examples of unsupervised learning include clustering and dimensionality reduction.
Answered by AI
Add your answer
Q2. What is sigmoid function
Ans. 
Sigmoid function is a mathematical function that maps any real value to a value between 0 and 1.
• Used in machine learning for binary classification problems to produce probabilities

• Commonly used in logistic regression

• Has an S-shaped curve

• Equation: f(x) = 1 / (1 + e^(-x))
Answered by AI
Add your answer

Q1. Always prepare projects u have worked on in STAR format.
Add your answer
Q2. Tell me about yourself .
Add your answer
Q3. Explain any 4 projects in STAR format
Ans. 
Developed a recommendation system for an e-commerce website
• Used collaborative filtering to recommend products to users

• Implemented the system using Python and Apache Spark

• Evaluated the system's performance using precision and recall metrics

• Improved the system's performance by incorporating user feedback
Answered by AI
Add your answer