Dhwani Rural Information Systems Data Analyst Interview Questions and Answers

Based on my CV, they assigned me a task related to data migration.

Q1. Project reviews
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Q2. Attendance regarding
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Q1. Give a brief about yourself.
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Q2. Salary Expectation and earliest joining date
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Q1. Brief about a project worked in the company.
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Q2. What is Data Leakage?
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Data leakage occurs when information from outside the training dataset is used to create a model, leading to unrealistic performance.
• Occurs when information that would not be available in a real-world scenario is used in the model training process

• Can result in overly optimistic performance metrics for the model

• Examples include using future data, target leakage, and data preprocessing errors
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Q3. What is Encoder Decoder? What is a Transformer model and explain its architecture?
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Encoder Decoder is a neural network architecture used for sequence-to-sequence tasks. Transformer model is a type of neural network architecture that relies entirely on self-attention mechanisms.
• Encoder Decoder is commonly used in machine translation tasks where the input sequence is encoded into a fixed-length vector representation by the encoder and then decoded into the target sequence by the decoder.

• Transformer mod...read more
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Q4. Name some Deep learning models?
Ans. 
Deep learning models include CNN, RNN, LSTM, GAN, and Transformer.
• Convolutional Neural Networks (CNN) - used for image recognition tasks

• Recurrent Neural Networks (RNN) - used for sequential data like time series

• Long Short-Term Memory (LSTM) - a type of RNN with memory cells

• Generative Adversarial Networks (GAN) - used for generating new data samples

• Transformer - used for natural language processing tasks
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Q5. What is Regularization in machine learning?
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Regularization is a technique used in machine learning to prevent overfitting by adding a penalty term to the model's loss function.
• Regularization helps to reduce the complexity of the model by penalizing large coefficients.

• It adds a penalty term to the loss function, which discourages the model from fitting the training data too closely.

• Common types of regularization include L1 (Lasso) and L2 (Ridge) regularization.

• Re...read more
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Q6. What is Model Quantization?
Ans. 
Model quantization is the process of reducing the precision of the weights and activations of a neural network model to improve efficiency.
• Reduces memory usage and speeds up inference by using fewer bits to represent numbers

• Can be applied to both weights and activations in a neural network model

• Examples include converting 32-bit floating point numbers to 8-bit integers
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