HCLTech Life Sciences Consultant Interview Questions and Answers

Abstract reasoning questions and numerical questions

Q1. Project related question and course related question
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Q2. Completed course related
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Q3. Any other course questions
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Q1. Hr round discuss about company
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Q2. Salary details and intensive details discuss the hr round
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Sorry I have no idea in topic of assignment details..

There were verbal, non verbal, reasoning , English and maths questions

Q1. Tell me about your project.
Ans. 
I worked on a project analyzing customer behavior using machine learning algorithms.
• Used Python for data preprocessing and analysis

• Implemented machine learning models such as decision trees and logistic regression

• Performed feature engineering to improve model performance
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Q2. What programming knowledge you have ?
Ans. 
Proficient in Python, R, and SQL with experience in data manipulation, visualization, and machine learning algorithms.
• Proficient in Python for data analysis and machine learning tasks

• Experience with R for statistical analysis and visualization

• Knowledge of SQL for querying databases and extracting data

• Familiarity with libraries such as Pandas, NumPy, Matplotlib, and Scikit-learn
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Q1. Where do you stay ?
Ans. 
I currently stay in an apartment in downtown area.
• I stay in an apartment in downtown area

• My current residence is in a city

• I live close to my workplace
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Q2. Tell me about you
Ans. 
I am a data science enthusiast with a strong background in statistics and machine learning.
• Background in statistics and machine learning

• Passionate about data science

• Experience with data analysis tools like Python and R
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Q1. Difference between fact and figure.
Ans. 
Fact is a statement that can be proven true or false, while figure is a numerical value or statistic.
• Fact is a statement that can be verified or proven true or false.

• Figure is a numerical value or statistic.

• Facts are objective and can be verified through evidence or research.

• Figures are quantitative data used to represent information.

• Example: 'The sky is blue' is a fact, while 'The average temperature is 25 degrees Cel
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Q2. Explain data modelling
Ans. 
Data modelling is the process of creating a visual representation of data to understand its structure, relationships, and patterns.
• Data modelling involves identifying entities, attributes, and relationships in a dataset.

• It helps in organizing data in a way that is easy to understand and analyze.

• Common data modelling techniques include Entity-Relationship (ER) diagrams and UML diagrams.

• Data modelling is essential for da...read more
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Q1. ML and deep learning questions
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Q1. Projects discussion
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Q2. Chatgpt architecture
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It was a MCQ based test

Q1. ML related question regression , classification , cost function and gradient descent
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Q2. Project related qs
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(51+52+53+......+100) =

Q1. M.s excel , red hat ,
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Q2. Introdution M.s word
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Q3. Linux , git , mavel , nexus.
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Q4. Sonar cube , aws , super putty.
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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.
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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
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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
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