AiDash Senior Data Scientist Interview Questions and Answers

Q1. Difference between attention and self attention
Ans. 
Attention focuses on specific parts of input data, while self attention considers relationships within the input data itself.
• Attention is used in models like seq2seq for machine translation to focus on relevant parts of the input sequence.

• Self attention is used in transformer models to capture dependencies between different words in a sentence.

• Attention mechanisms can be either global or local, while self attention is
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Q2. Project related questions
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Q3. Feature engineering
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Q4. Handling null values
Ans. 
Handling null values is crucial for data integrity and analysis.
• Identify null values in the dataset using functions like isnull() or isna()

• Decide on the best strategy to handle null values - imputation, deletion, or flagging

• Impute missing values using mean, median, mode, or predictive modeling techniques

• Delete rows or columns with a high percentage of missing values if they cannot be imputed

• Flag null values to distingu
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Q5. Handling imbalanced training data
Ans. 
Handling imbalanced training data is crucial for model performance and accuracy.
• Use techniques like oversampling, undersampling, or SMOTE to balance the dataset

• Utilize algorithms that are robust to imbalanced data, such as Random Forest or XGBoost

• Consider using ensemble methods or cost-sensitive learning to address class imbalance
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Q6. Overfitting optimize
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Q7. What is text embeddings
Ans. 
Text embeddings are numerical representations of text data that capture semantic meaning.
• Text embeddings convert words or sentences into numerical vectors.

• They are used in natural language processing tasks like sentiment analysis, text classification, and machine translation.

• Popular techniques for generating text embeddings include Word2Vec, GloVe, and BERT.
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Q1. Question From Regression, classification and clustering Algorithm.
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Q2. Bagging and boosting
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Q3. How to handel less dataset for regression problems
Ans. 
Use techniques like regularization, feature selection, cross-validation, and data augmentation.
• Utilize regularization techniques like Lasso or Ridge regression to prevent overfitting.

• Perform feature selection to focus on the most important variables and reduce noise.

• Use cross-validation to assess model performance and generalizability.

• Consider data augmentation techniques like synthetic data generation or bootstrapping...read more
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Q4. Python Coding and some scenario based ML question.
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Q1. Question from My project and mentioned skills in resume
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Q2. How will you lead team and what your ways.
Ans. 
I will lead by setting clear goals, providing guidance, fostering collaboration, and recognizing team achievements.
• Set clear goals and expectations for the team

• Provide guidance and support to team members

• Foster collaboration and communication within the team

• Recognize and reward team achievements

• Lead by example and demonstrate strong work ethic
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Q1. Salary and Notice period
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Q1. Deep questions about Machine learning, Deep learning, Generative AI, the working of transformers etc.
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Q1. Deep questions about Machine learning and deep learning with projects done. This was a client round.
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Q1. Salary discussion, project discussion, why change? Why Wipro
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Q1. MLOps vs DevOps
Ans. 
MLOps focuses on machine learning model deployment and management, while DevOps focuses on software development and IT operations.
• MLOps is specifically tailored for machine learning models, ensuring they are deployed, monitored, and managed effectively.

• DevOps is a broader practice that focuses on collaboration between development and operations teams to automate and streamline the software development process.

• MLOps inc...read more
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Q1. Docker Containers
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Q1. ML Deployment, Docker Containers
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Q1. Compensation, Fixed Salary
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Q1. Coding in Python using explode
Ans. 
Explode function in Python is used to split a string into a list of strings based on a specified delimiter.
• Use the explode function from the pandas library to split a string column into multiple rows in a DataFrame.

• Specify the delimiter parameter to define how the string should be split.

• For example, df['column'].str.split('delimiter').explode() will split the strings in 'column' based on 'delimiter' and create a new ro
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Will be two questions to solve

Q1. Details about ML,DL
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Q2. Detials about Projects
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Q1. Basics of Machine Learning, Gradient Descent, Trade-off, etc.
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It was based on logical reasoning and verbal along with speech recognition

Python based basic questions like sort find maximum

Q1. They asked all the questions based on my projects.
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Q1. Simple he procedure
Ans. 
Simple procedure refers to a straightforward and easy-to-follow set of steps or instructions.
• Follow a clear sequence of steps

• Use simple language and visuals if needed

• Ensure the procedure is easy to understand and execute
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Sql and basic ML , statistics questions

Q1. 4 one on one rounds with case studies and ML questions
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