Blackstraw AI Data Scientist Interview Questions, Process, and Tips

General aptitude basics

Mcq and basic ml model building

Q1. Explain Transformers how different from previous RNN, LSTM etc.
Ans. 
Transformers are a type of neural network architecture that utilizes self-attention mechanisms to process sequential data.
• Transformers use self-attention mechanisms to weigh the importance of different input elements, allowing for parallel processing of sequences.

• Unlike RNNs and LSTMs, Transformers do not rely on sequential processing, making them more efficient for long-range dependencies.

• Transformers have been shown ...read more
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Q2. What are different types of Attention?
Ans. 
Different types of Attention include self-attention, global attention, and local attention.
• Self-attention focuses on relationships within the input sequence itself.

• Global attention considers the entire input sequence when making predictions.

• Local attention only attends to a subset of the input sequence at a time.

• Examples include Transformer's self-attention mechanism, Bahdanau attention, and Luong attention.
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Q3. Difference between GPT and BERT model
Ans. 
GPT is a generative model while BERT is a transformer model for natural language processing.
• GPT is a generative model that predicts the next word in a sentence based on previous words.

• BERT is a transformer model that considers the context of a word by looking at the entire sentence.

• GPT is unidirectional, while BERT is bidirectional.

• GPT is better for text generation tasks, while BERT is better for understanding the cont
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Q1. Difference between Data scientist, ML and AI
Ans. 
Data scientists analyze data to gain insights, machine learning (ML) involves algorithms that improve automatically through experience, and artificial intelligence (AI) refers to machines mimicking human cognitive functions.
• Data scientists analyze large amounts of data to uncover patterns and insights.

• Machine learning involves developing algorithms that improve automatically through experience.

• Artificial intelligence r...read more
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First round is coding round where two use cases are there. Need to solve them

Q1. They will all topics Statistics, SQL, Python, Machine Learning, Data Science
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Q1. They will discuss more on the projects what we worked on
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Q1. Salary Discussion
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Asked 2 to 3 python coding question...

Q1. What is random forest
Ans. 
Random forest is an ensemble learning method used for classification and regression tasks.
• Random forest is a collection of decision trees that are trained on random subsets of the data.

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

• Random forest is robust to overfitting and noisy data, and can handle large datasets wi...read more
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Q2. Explain lasso in feature selection
Ans. 
Lasso is a feature selection technique that penalizes the absolute size of the regression coefficients.
• Lasso stands for Least Absolute Shrinkage and Selection Operator

• It adds a penalty term to the regression equation, forcing some coefficients to be exactly zero

• Helps in selecting the most important features and reducing overfitting

• Useful when dealing with high-dimensional data

• Example: In a dataset with multiple feature...read more
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