Wipro Data Scientist and Machine Learning Engineer Interview Questions and Answers

Q1. Basic ML questions and previous project-related queries
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Q1. Advanced ML questions
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Q1. Project handle queires
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Q1. Which GenAI projects I have worked on
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Q2. What is the context window in LLMs
Ans. 
Context window in LLMs refers to the number of surrounding words considered when predicting the next word in a sequence.
• Context window helps LLMs capture dependencies between words in a sentence.

• A larger context window allows the model to consider more context but may lead to increased computational complexity.

• For example, in a context window of 2, the model considers 2 words before and 2 words after the target word fo
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Q3. What is top_k parameter
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top_k parameter is used to specify the number of top elements to be returned in a result set.
• top_k parameter is commonly used in machine learning algorithms to limit the number of predictions or recommendations.

• For example, in recommendation systems, setting top_k=5 will return the top 5 recommended items for a user.

• In natural language processing tasks, top_k can be used to limit the number of possible next words in a
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Q4. What are regex patterns in python
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Regex patterns in Python are sequences of characters that define a search pattern.
• Regex patterns are used for pattern matching and searching in strings.

• They are created using the 're' module in Python.

• Examples of regex patterns include searching for email addresses, phone numbers, or specific words in a text.
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Q5. What are iterators and tuples
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Iterators are objects that allow iteration over a sequence of elements. Tuples are immutable sequences of elements.
• Iterators are used to loop through elements in a collection, like lists or dictionaries

• Tuples are similar to lists but are immutable, meaning their elements cannot be changed

• Example of iterator: for item in list: print(item)

• Example of tuple: my_tuple = (1, 2, 3)
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Q6. Do I have REST API experience
Ans. 
Yes, I have experience working with REST APIs in various projects.
• Developed RESTful APIs using Python Flask framework

• Consumed REST APIs in data analysis projects using requests library

• Used Postman for testing and debugging REST APIs
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Q1. About Machine learning basics, activation functions linear regression, cnn, all basics..
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Q2. About project questions, about sdlc basic 3 questions
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Q3. About Why not used another model for training?
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Q1. Tell me about your self?
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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
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Confusion matrix what are your job rolls explain me Gradient boosting algorithm?

Q1. Number of Duplicate words in a string
Ans. 
Count the number of duplicate words in a string.
• Split the string into words using a delimiter like space or punctuation.

• Create a dictionary to store the count of each word.

• Iterate through the words and increment the count in the dictionary.

• Count the number of words with count greater than 1 as duplicates.
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Q2. Chunking in LLM
Ans. 
Chunking in LLM refers to breaking down text into smaller chunks for better processing by the language model.
• Chunking helps improve the efficiency of the language model by breaking down large text inputs into smaller segments.

• It can help the model better understand the context and relationships within the text.

• Chunking is commonly used in natural language processing tasks such as text summarization and sentiment analys
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Basic DP, Array Questions

Q1. Resume Walkthrough and Discussion, Medium level coding questions
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Q1. Discussion with Manager
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Q1. Normal HR round
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Q1. Questions related to machine learning
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Q1. Question related to machine learning, projects and case study.
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Q1. Salary discussion and position offered.
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Q1. How do you handle outliers? How to handle imbalance dataset? Feature engineering techniques?
Ans. 
Outliers can be handled by removing, transforming or imputing them. Imbalanced datasets can be handled by resampling techniques. Feature engineering involves creating new features from existing ones.
• Outliers can be removed using statistical methods like z-score or IQR.

• Outliers can be transformed using techniques like log transformation or box-cox transformation.

• Outliers can be imputed using techniques like mean imputat...read more
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Q1. 1. Explain the project in detail 2. Explain me your 5 favourite models 3. Questions on probability
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Q1. It was a HR round and HR has asked me what's your salary expectations.
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