Salesforce 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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2 coding questions - one on binary tree and list operations.

Q1. Xgboost explanation
Ans. 
Xgboost is a popular machine learning algorithm known for its speed and performance in handling large datasets.
• Xgboost stands for eXtreme Gradient Boosting, which is an implementation of gradient boosted decision trees.

• It is widely used in Kaggle competitions and other machine learning tasks due to its efficiency and accuracy.

• Xgboost is known for its ability to handle missing data, regularization techniques, and parall...read more
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Q2. Data structures question compulsory
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Q1. Explain L1 & L2 regularization
Ans. 
L1 & L2 regularization are techniques used in machine learning to prevent overfitting by adding a penalty term to the cost function.
• L1 regularization adds the absolute values of the coefficients as penalty term (Lasso regression)

• L2 regularization adds the squared values of the coefficients as penalty term (Ridge regression)

• L1 regularization encourages sparsity in the model, while L2 regularization tends to shrink the c...read more
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Q2. Explain error metric
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Error metric is a measure used to evaluate the performance of a model by comparing predicted values to actual values.
• Error metric quantifies the difference between predicted values and actual values.

• Common error metrics include Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared.

• Lower values of error metric indicate better performance of the model.

• Error metric helps in und...read more
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Q3. Where do you see yourself
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Q1. Resume related questions on project experiences
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Q2. Check technical stack, whether you have the right tech skills
Ans. 
Yes, I have the right tech skills for the Data Scientist role.
• Proficient in programming languages like Python, R, and SQL

• Experience with data visualization tools like Tableau or Power BI

• Knowledge of machine learning algorithms and statistical analysis techniques

• Familiarity with big data technologies like Hadoop and Spark
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Q1. Basic ml concept
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Q2. Details of machine learning projects and how to deal with communication, prioritization issues
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Simple leetcode type sql, python questions

Q1. How do you work towards a random forest?
Ans. 
To work towards a random forest, you need to gather and preprocess data, select features, train individual decision trees, and combine them into an ensemble.
• Gather and preprocess data from various sources

• Select relevant features for the model

• Train individual decision trees using the data

• Combine the decision trees into an ensemble

• Evaluate the performance of the random forest model
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Q2. What is bias variance trade-off
Ans. 
Bias-variance trade-off is the balance between overfitting and underfitting in a model.
• Bias is the error due to assumptions made in the learning algorithm. Variance is the error due to sensitivity to small fluctuations in the training set.

• High bias leads to underfitting, while high variance leads to overfitting.

• The goal is to find the sweet spot where the model has low bias and low variance, which results in good gener...read more
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Q1. Tell me about your self
Ans. 
I am a data scientist with expertise in machine learning and data analysis.
• I have a strong background in statistics and mathematics.

• I am proficient in programming languages such as Python and R.

• I have experience working with large datasets and extracting insights from them.

• I have developed predictive models for various industries, including finance and e-commerce.

• I am skilled in data visualization and communicating com
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Q2. Why you are perfect for this job
Ans. 
I have a strong background in data analysis and machine learning, with a proven track record of delivering actionable insights.
• I have a Master's degree in Data Science and have completed several projects involving data analysis and predictive modeling.

• I am proficient in programming languages such as Python and R, as well as in using tools like TensorFlow and Tableau.

• I have experience working with large datasets and hav...read more
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