Kloud 9 Data Scientist Interview Questions and Answers

Q1. Python Coding, Data Processing
Add your answer

Q1. Deep Learning - Transformers, Speechbrain, Machine Learning, projects
Add your answer

Q1. Why do you want to join? Reason for switch.
Add your answer

M maximum numbers from array.
Merge sort

Q1. Build Recommendation system
Ans. 
A recommendation system suggests items to users based on preferences and behaviors.
• Collaborative Filtering: Uses user-item interactions. Example: Netflix recommending shows based on user viewing history.

• Content-Based Filtering: Recommends items similar to those a user liked. Example: Spotify suggesting songs based on user playlists.

• Hybrid Systems: Combines collaborative and content-based methods. Example: Amazon sugges...read more
Answered by AI
Add your answer
Q2. Top products for user queries
Ans. 
Top products for user queries can be determined by analyzing search logs and user behavior.
• Analyze search logs to identify frequently searched products

• Use machine learning algorithms to predict user preferences

• Consider user behavior and purchase history to recommend products

• Examples: Amazon's 'Frequently Bought Together' and 'Customers Who Bought This Item Also Bought' sections
Answered by AI
Add your answer

Q1. Why do I want to join,
Add your answer
Q2. What kind of work I did in past
Add your answer

Q1. Explain basic ML model
Ans. 
A basic machine learning model is a mathematical algorithm that learns patterns from data to make predictions or decisions.
• Uses labeled data to train the model

• Can be supervised or unsupervised

• Examples include linear regression, decision trees, and k-means clustering
Answered by AI
Add your answer

Q1. Supervised machine learning application
Ans. 
Supervised machine learning involves training a model on labeled data to make predictions or classifications.
• Supervised machine learning requires labeled data for training

• Common algorithms include linear regression, decision trees, and support vector machines

• Examples: predicting housing prices based on features like location and size, classifying emails as spam or not spam
Answered by AI
Add your answer

Q1. Can you describe the projects you have worked on?
Ans. 
I have worked on various data science projects, including predictive modeling, natural language processing, and image classification.
• Developed a predictive model for customer churn using logistic regression, achieving an accuracy of 85%.

• Implemented a natural language processing project to analyze customer feedback, resulting in actionable insights for product improvement.

• Created an image classification model using conv...read more
Answered by AI
Add your answer
Q2. Machine learning and Statistics Questions- Supervised,unsupervised,feature selection techniques, overfitting,underfitting, Hypothesis testing
Add your answer
Q3. Can you describe a specific instance in which you applied data-driven analysis to a business project and how you coordinated and persuaded team members to take specific actions to enhance the model's perfo...read more
Ans. 
Led a data-driven project to improve customer retention using predictive modeling and team collaboration.
• Identified key metrics for customer retention through exploratory data analysis.

• Developed a predictive model using logistic regression to forecast churn.

• Presented findings to the team, emphasizing the potential impact on revenue.

• Facilitated workshops to gather feedback and refine the model based on team insights.

• Use...read more
Answered by AI
Add your answer
Q4. Sql coding questions, retrieve emp with max salary, retrieve name of employees who join in last 6 months. python code - write a function to calculate mean and variance of data without using builtin functio...read more
Add your answer

Q1. Related to basic machine learning algorithms, python basics
Add your answer

Q1. Explain LSTM networking architect
Ans. 
LSTM networks are a type of recurrent neural network designed to learn long-term dependencies in sequential data.
• LSTM stands for Long Short-Term Memory, which helps in remembering information for long periods.

• It consists of memory cells that can maintain information over time, addressing the vanishing gradient problem.

• Key components include input gates, output gates, and forget gates that control the flow of informatio...read more
Answered by AI
Add your answer
Q2. Why you want to work for us
Ans. 
I admire your innovative approach to data science and your commitment to impactful solutions in the industry.
• Your company's focus on leveraging data for social good aligns with my personal values, as seen in your recent project on healthcare analytics.

• I am impressed by your collaborative culture, which fosters creativity and innovation, evident in your team's successful launch of the new AI tool.

• The opportunity to work...read more
Answered by AI
Add your answer

Basic pytghon coding

Q1. Discussion about projects done and related data science topics on nlp
Add your answer

Q1. Discussion regarding why you want to join Tata IQ
Add your answer