Snowflake Data Scientist Interview Questions and Answers

Q1. How does fbpropher forecasting model works and how is can be used to forecsst trafffic
Ans. 
fbprophet is a forecasting model developed by Facebook that uses time series data to make predictions.
• fbprophet is an open-source forecasting tool developed by Facebook's Core Data Science team.

• It is based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects.

• fbprophet can be used to forecast traffic by providing historical data on traffic patterns and usi...read more
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Q1. How can Logistic regression be applied for multiclasstext classification
Ans. 
Logistic regression can be applied for multiclasstext classification by using one-vs-rest or softmax approach.
• One-vs-rest approach: Train a binary logistic regression model for each class, treating it as the positive class and the rest as the negative class.

• Softmax approach: Use the softmax function to transform the output of the logistic regression into probabilities for each class.

• Evaluate the model using appropriate...read more
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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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Standard question from sql and python in hackerrank

Q1. Reverse a linked list
Ans. 
Reverse a linked list by changing the direction of pointers
• Start with three pointers: current, previous, and next

• Iterate through the linked list, updating pointers to reverse the direction

• Return the new head of the reversed linked list
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Q2. Question based on joins and subquery
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Q1. More question about project
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Q2. What do you know about genAI
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Q1. Basic ml related questions,past project discussion
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Q1. AI, ML related, statistical analysis of the graph.
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Q1. Work culture, check
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Q1. Work culture, project discussion, scenario based questions
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Q1. Basic HR questions, why sap salary discussions
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DSA,ML,SQL,stats,DL,

Q1. What are hyperparameters in random forest
Ans. 
Hyperparameters in random forest are parameters that are set before the learning process begins.
• Hyperparameters control the behavior of the random forest algorithm.

• They are set by the data scientist and are not learned from the data.

• Examples of hyperparameters in random forest include the number of trees, the maximum depth of trees, and the number of features considered at each split.
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Q2. How to do QnA system with LLM
Ans. 
A QnA system with LLM is a system that uses the Language Model for Information Retrieval and Question Answering.
• Preprocess the input question and convert it into a format suitable for the LLM model.

• Fine-tune the LLM model on a dataset of question-answer pairs.

• Use the fine-tuned model to generate answers for new questions.

• Evaluate the performance of the QnA system using metrics like precision, recall, and F1 score.

• Itera...read more
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Q3. How to do unit testing
Ans. 
Unit testing is a process of testing individual units of code to ensure they function correctly.
• Write test cases for each unit of code

• Test inputs, outputs, and edge cases

• Use testing frameworks like JUnit or pytest

• Automate tests to run regularly

• Ensure tests are independent, isolated, and repeatable
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