Comviva Technology Data Scientist Interview Questions and Answers

Coding and AI related Questions

Q1. Detail technical questions on projects
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Q1. When do we use PCA?
Ans. 
PCA is used to reduce the dimensionality of data by finding the most important features.
• PCA is used when dealing with high-dimensional data to reduce the number of features and avoid multicollinearity.

• It helps in visualizing data in lower dimensions while retaining as much variance as possible.

• PCA is commonly used in image processing, genetics, finance, and other fields where dimensionality reduction is needed.
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Q2. PCA is a dimensionality reduction technique and is used to reduce the less useful features from the dataset
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Q1. LTTS data science interview
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Questions related to Pandas, List, String

Q1. 1) How decision tree works 2) what are the parameters used in OpenCV?
Ans. 
Decision tree is a tree-like model used for classification and regression. OpenCV parameters include image processing and feature detection.
• Decision tree is a supervised learning algorithm that recursively splits the data into subsets based on the most significant attribute.

• It is used for both classification and regression tasks.

• OpenCV parameters include image processing techniques like smoothing, thresholding, and mor...read more
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Q1. Coding question of finding index of 2 nos. having total equal to target in a list, without using nested for loop? l= [2,15,5,7] t= 9 output》》[0,3]
Ans. 
Finding index of 2 numbers having total equal to target in a list without nested for loop.
• Use dictionary to store the difference between target and each element of list.

• Iterate through list and check if element is in dictionary.

• Return the indices of the two elements that add up to target.
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Q2. What is random forest, knn?
Ans. 
Random forest and KNN are machine learning algorithms used for classification and regression tasks.
• Random forest is an ensemble learning method that constructs multiple decision trees and combines their outputs to make a final prediction.

• KNN (k-nearest neighbors) is a non-parametric algorithm that classifies new data points based on the majority class of their k-nearest neighbors in the training set.

• Random forest is us...read more
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Q1. Ll coding on python dictionary
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Q2. Find unique keys in 2 dictionaries
Ans. 
To find unique keys in 2 dictionaries.
• Create a set of keys for each dictionary

• Use set operations to find the unique keys

• Return the unique keys
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Q3. Aws ec2 model deployment procedure
Ans. 
AWS EC2 model deployment involves creating an instance, installing necessary software, and deploying the model.
• Create an EC2 instance with the desired specifications

• Install necessary software and dependencies on the instance

• Upload the model and any required data to the instance

• Deploy the model using a web server or API

• Monitor the instance and model performance for optimization
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Q4. Overloading concept of oop
Ans. 
Overloading is the ability to define multiple methods with the same name but different parameters.
• Overloading allows for more flexibility in method naming and improves code readability.

• Examples include defining multiple constructors for a class with different parameter lists or defining a method that can accept different data types as input.

• Overloading is resolved at compile-time based on the number and types of argume...read more
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SQL, select statment and DDL commands

Q1. Python lists and conditional statements and loos
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Q1. Explain the importance of Model Evaluation in the process of ML pipeline
Ans. 
Model evaluation is crucial in ML pipeline to assess the performance and generalization of the model.
• Helps in selecting the best model for the given problem by comparing different models based on metrics like accuracy, precision, recall, etc.

• Prevents overfitting by checking if the model is performing well on unseen data.

• Guides in fine-tuning hyperparameters to improve model performance.

• Enables understanding of model li...read more
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Expect technical questions as well as moderate level coding questions

Q1. Data science and MLOps concepts
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Q1. Behavioral and managerial rounds
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Q1. Difference between .py and .pyc
Ans. 
The .py files contain Python code, while the .pyc files are compiled bytecode files generated by Python when a .py file is imported.
• The .py files are human-readable text files containing Python code.

• The .pyc files are compiled bytecode files created by Python to improve execution speed.

• The .pyc files are automatically generated by Python when a .py file is imported.

• The .pyc files are platform-independent and can be dis
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Q2. Why we use bias in neural networks? Why do we scale our data in neural networks?
Ans. 
Bias in neural networks helps in capturing the underlying patterns in data. Scaling data helps in improving convergence and performance.
• Bias in neural networks helps in shifting the activation function to better fit the data.

• It allows the model to capture the underlying patterns in the data by providing flexibility in the decision boundary.

• Scaling data helps in improving convergence by ensuring that the gradients are o...read more
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Q3. Explain RNN and LSTM
Ans. 
RNN is a type of neural network that processes sequential data. LSTM is a type of RNN that can learn long-term dependencies.
• RNN stands for Recurrent Neural Network and is designed to handle sequential data by maintaining a hidden state that captures information about previous inputs.

• LSTM stands for Long Short-Term Memory and is a type of RNN that addresses the vanishing gradient problem by introducing a memory cell, in...read more
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