Salesforce Lead Data Analyst Interview Questions and Answers

Q1. Asked Algorithms used in the project (No follow-up on mentioned algorithms, cut-off mid-explanation of business problem, scale, and solution wanting to know just the names of the algorithm) - Answered by n...read more
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Q2. Count all pairs of numbers from a list where the ending digit of the ith number equals the starting digit of the jth number. Example [122, 21, 21, 23] should have 5 pairs (122, 21), (122, 21), (122, 23), (...read more
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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
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Q3. Interpretation of graphs, the first graph had perpendicular lines from the error to the fitted line and the second graph had lines from the error to the fitted line, parallel to the y-axis. - Interpreted t...read more
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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
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Q4. What does np.einsum() do
Ans. 
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)
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Q5. How to generate random numbers using numpy, what is the difference between numpy.random.rand and numpy.random.randn
Ans. 
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
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Q6. Difference between logit and probabilities in deep learning
Ans. 
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
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It was related to Aptitude MCQ and 2-coding test

Q1. Palindrome of a number
Ans. 
A palindrome of a number is a number that remains the same when its digits are reversed.
• To check if a number is a palindrome, reverse the number and compare it with the original number.

• Examples: 121 is a palindrome, 123 is not a palindrome.
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Q2. Merge 2linked list
Ans. 
Merging two linked lists involves combining the elements of both lists into a single list.
• Create a new linked list to store the merged elements

• Traverse through both linked lists and add elements to the new list

• Handle cases where one list is longer than the other
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Q3. About projects you have done
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Q1. Explain your resume fully
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Q2. General questions on the behaviour
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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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Q1. Question about Googlyness
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Q2. Question about Google Lens, a RCA question
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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. Given a situation how do you handle different cases
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Q2. Given a proab stats question
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Q1. Do you know power BI
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Q2. Yes. I know PowerBI
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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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