Salesforce Lead Data Scientist Interview Questions and Answers

Count pairs of numbers where ending digit of ith number equals starting digit of jth number.

• Iterate through each pair of numbers in the list

• Check if the ending digit of the ith number equals the starting digit of the jth number

• Increment the count if the condition is met

Interpretation of graphs in linear regression analysis

• Perpendicular lines from error to fitted line in first graph indicate OLS using projection matrices

• Lines parallel to y-axis from error to fitted line in second graph suggest evaluation of linear regression to y-pred - y-actual method

• PCA could also be a possible interpretation for the second graph

np.einsum() performs Einstein summation on arrays.

• Performs summation over specified indices

• Can also perform other operations like multiplication, contraction, etc.

• Syntax: np.einsum(subscripts, *operands)

numpy.random.rand generates random numbers from a uniform distribution, while numpy.random.randn generates random numbers from a standard normal distribution.

• numpy.random.rand generates random numbers from a uniform distribution between 0 and 1.

• numpy.random.randn generates random numbers from a standard normal distribution with mean 0 and standard deviation 1.

• Example: np.random.rand(3, 2) will generate a 3x2 array of r...read more

Logit is the log-odds of the probability, while probabilities are the actual probabilities of an event occurring.

• Logit is the natural logarithm of the odds ratio, used in logistic regression.

• Probabilities are the actual likelihood of an event occurring, ranging from 0 to 1.

• In deep learning, logit values are transformed into probabilities using a softmax function.

• Logit values can be negative or positive, while probabili

Preprocessing large/small datasets involves cleaning, transforming, and organizing data to prepare it for analysis.

• Remove duplicates and missing values

• Normalize or standardize numerical features

• Encode categorical variables

• Feature scaling

• Handling outliers

• Dimensionality reduction techniques like PCA

• Splitting data into training and testing sets

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

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

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