ServiceNow Data Scientist Interview Questions and Answers

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. 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. 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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Q1. Your past experience related questions
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Q1. Role related questions
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Based on topological sort

Q1. Ml baiscs like LR, SVM
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Q1. Ml design questions were asked
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Q1. Why do you want to join
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Q1. Explain Decision Trees
Ans. 
Decision Trees are a popular machine learning algorithm used for classification and regression tasks.
• Decision Trees are a tree-like structure where each internal node represents a feature or attribute, each branch represents a decision rule, and each leaf node represents the outcome.

• They are easy to interpret and visualize, making them popular for exploratory data analysis.

• Decision Trees can handle both numerical and c...read more
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Q2. How will you finetune LLMs
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LLMs can be finetuned by adjusting hyperparameters, training on specific datasets, and using techniques like transfer learning.
• Adjust hyperparameters such as learning rate, batch size, and number of layers to improve performance.

• Train the LLM on domain-specific datasets to improve its understanding of specialized language.

• Utilize transfer learning by starting with a pre-trained LLM model and fine-tuning it on a smaller...read more
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