Calsoft Lead Data Scientist Interview Questions and Answers

It was a test of 90 minutes containing almost every thing .Quantitative+Logical+Computer fundamentals+2 coding questions

DURATION 1.3 HRS WITH REASONING AND 2 CODING QUESTION

Q1. SQL QUESTION FIND N TH HIGHEST SALARY
Ans. 
Use SQL query with ORDER BY and LIMIT to find the Nth highest salary.
• Use ORDER BY clause to sort salaries in descending order

• Use LIMIT to specify the Nth highest salary

• Example: SELECT salary FROM employees ORDER BY salary DESC LIMIT N,1
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Q2. SQL QUESTION PIVOT TABLE QUESTION
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Q1. What are the key topics related to Generative AI, specifically focusing on Retrieval-Augmented Generation (RAG) and Large Language Models (LLM)?
Ans. 
Key topics in Generative AI include Retrieval-Augmented Generation (RAG) and Large Language Models (LLM).
• RAG combines generative models with retrieval mechanisms to improve text generation.

• LLMs like GPT-3 and BERT are pre-trained on large text corpora to generate human-like text.

• Ethical considerations in using LLMs for text generation, such as bias and misinformation.

• Applications of RAG and LLMs in natural language pro...read more
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Q2. What is your understanding of the existing project and the technology stacks used?
Ans. 
I have a strong understanding of the existing project and the technology stacks used.
• The existing project is a data analytics platform used for analyzing customer behavior and making data-driven decisions.

• The technology stacks used include Python for data processing, SQL for database management, and Tableau for data visualization.

• I have experience working with machine learning algorithms such as regression, classificat
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Q1. What has been your contribution to existing work and the technologies used in that context?
Ans. 
I have contributed to improving data processing efficiency by implementing advanced machine learning algorithms and optimizing existing models.
• Implemented advanced machine learning algorithms to improve predictive accuracy

• Optimized existing models to increase efficiency and reduce processing time

• Developed automated data processing pipelines to streamline workflow

• Collaborated with cross-functional teams to integrate new...read more
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Q2. ML, DL, Gen AI implementation and flow
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Q3. Python Coding logic and semantics
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Q1. What are the existing work challenges you face, and what solutions have you implemented to address them?
Ans. 
Work challenges and solutions in data science role
• One challenge is handling large volumes of data efficiently, solution is implementing parallel processing techniques like MapReduce

• Another challenge is ensuring data quality, solution is developing data validation processes and implementing data cleaning algorithms

• Dealing with complex algorithms is a challenge, solution is continuous learning and staying updated with la
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Q2. What is your understanding of Generative AI and Natural Language Processing (NLP), and can you provide examples of their use cases?
Ans. 
Generative AI and NLP are advanced technologies used to create content and understand human language.
• Generative AI involves creating new content, such as images, music, or text, using algorithms like GANs.

• NLP focuses on understanding and generating human language, used in chatbots, sentiment analysis, and language translation.

• Use cases of Generative AI include deepfake videos, art generation, and text generation.

• NLP is...read more
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Q1. Introduce yourself
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Easy question. Easy to clear the round

Q1. One to one technical questions based on SQL and python.
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Basic data structures, puzzles questions and statistics problems

Q1. What was your previous company project
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Q2. What are different types of indexing
Ans. 
Different types of indexing include primary indexing, secondary indexing, clustered indexing, and non-clustered indexing.
• Primary indexing: Index based on the primary key of a table, typically implemented using a B-tree structure.

• Secondary indexing: Index based on a non-primary key column, allowing for faster retrieval of data based on that column.

• Clustered indexing: Physically reorders the table based on the indexed co...read more
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Q3. How does random forest work
Ans. 
Random forest is an ensemble learning method that builds multiple decision trees and merges their predictions.
• Random forest creates a set of decision trees from randomly selected subsets of the training data.

• Each tree in the random forest independently predicts the outcome, and the final prediction is made by averaging the predictions of all the trees.

• Random forest is effective in handling high-dimensional data and can...read more
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Q4. Explain over fitting
Ans. 
Overfitting occurs when a model learns the noise in the training data rather than the underlying pattern.
• Overfitting happens when a model is too complex and captures noise in the training data.

• It leads to poor generalization to new, unseen data.

• Regularization techniques like L1/L2 regularization can help prevent overfitting.

• Cross-validation can be used to detect and prevent overfitting.

• Example: A decision tree with too...read more
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Q5. Explain macros inExcel
Ans. 
Macros in Excel are automated sequences of commands that can be created to perform repetitive tasks.
• Macros can be recorded or written using Visual Basic for Applications (VBA)

• They can automate tasks such as formatting, data manipulation, and calculations

• Macros can be assigned to buttons or keyboard shortcuts for easy access

• They can save time and reduce errors in repetitive tasks
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Though I applied for Data Analyst position through campus Recruitment Program (Kolkata). It had MCQs and Coding test, but surprisingly the MCQs were about Java, OOPs concept and basic coding questions. Hardly any ML or Analytics related like Python, SQL or BI tools related questions. As if I appeared for SDE role, not sure why it happened, but later I came to know they hired none from our college.

This did not happen, as they didn't call anyone from our college.

Fundamental aptitude questions.

Two medium-level questions.