Innovaccer Senior Data Scientist Interview Questions and Answers

Q1. Basicn details to check for qualifications
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Q1. About my projects
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Q1. More details about ML models
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Questions related to Python and SQL, as well as a few multiple-choice questions in data science.

Q1. Project Explanation as well query related to project
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Q2. Machine learning basic question
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It contain both Aptitude and Coding about base models and Deep learning too

Q1. What different models technique ?
Ans. 
Different models techniques include linear regression, decision trees, random forests, support vector machines, and neural networks.
• Linear regression is used for predicting continuous values.

• Decision trees are used for classification and regression tasks.

• Random forests are an ensemble method based on decision trees.

• Support vector machines are used for classification tasks.

• Neural networks are used for complex pattern re
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Q2. What are performance metric where to use what?
Ans. 
Different performance metrics are used for different types of machine learning models to evaluate their effectiveness.
• For classification models, metrics like accuracy, precision, recall, F1 score, and ROC-AUC are commonly used.

• For regression models, metrics like mean squared error (MSE), mean absolute error (MAE), and R-squared are commonly used.

• For clustering models, metrics like silhouette score and Davies-Bouldin in...read more
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Q1. Explain about Project
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Q2. What are problems faced in that project?
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Q1. About projects and then questions related to ML and DL. Mostly focused on DL part
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Q2. What is the difference between Adam optimizer and Gradient Descent Optimizer?
Ans. 
Adam optimizer is an extension to the Gradient Descent optimizer with adaptive learning rates and momentum.
• Adam optimizer combines the benefits of both AdaGrad and RMSProp optimizers.

• Adam optimizer uses adaptive learning rates for each parameter.

• Gradient Descent optimizer has a fixed learning rate for all parameters.

• Adam optimizer includes momentum to speed up convergence.

• Gradient Descent optimizer updates parameters b...read more
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Q3. When to use Relu and when not?
Ans. 
Use ReLU for hidden layers in deep neural networks, avoid for output layers.
• ReLU is commonly used in hidden layers to introduce non-linearity and speed up convergence.

• Avoid using ReLU in output layers for regression tasks as it can lead to vanishing gradients.

• Consider using Leaky ReLU or Sigmoid for output layers depending on the task.

• ReLU is computationally efficient and helps in preventing the vanishing gradient prob...read more
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Q1. Asked about projects. What is classification? Is knn used for regression?how? decision tree working for regression and classification Is naive Bayes used for regression?how? LLM Docker Aws GenAI Code for ...read more
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Project regarding data science

Sql and python questions were there with basic logic check

Q1. Python code with funticion
Ans. 
Python code with function
• Define a function using 'def' keyword

• Include parameters inside parentheses

• Use 'return' statement to return a value from the function
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Q2. Sql with case statwment
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Q1. Questions related to the projects in the resume and some questions on machine learning concepts.
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