Oracle Research Scientist Interview Questions and Answers

Basic self evaluation test.

Q1. What project I have completed and follow-up questions on that?
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Q2. How to handle class imbalance.
Ans. 
Handling class imbalance involves techniques like resampling, using different algorithms, and adjusting class weights.
• Use resampling techniques like oversampling or undersampling to balance the classes.

• Utilize algorithms that are robust to class imbalance, such as Random Forest, XGBoost, or SVM.

• Adjust class weights in the model to give more importance to minority class.

• Use evaluation metrics like F1 score, precision, r...read more
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Q3. Basic Python coding questions.
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Q1. Data-related questions.
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Q2. ML Ops questions.
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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
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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
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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
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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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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. Asked about projects. What is classification? Is knn used for regression?how? decision tree working for regression and classification Is naive Bayes used for regression?how? LLM Docker Aws GenAI Code for ...read more
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Python Interview questions on list and vectors

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. Basic DS question like how to handle missing features
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Q1. Case study based questions
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Q1. Difference between ridge and lasso in regression
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Ridge and Lasso are regularization techniques in regression to prevent overfitting by adding penalty terms to the cost function.
• Ridge regression adds a penalty term equivalent to the square of the magnitude of coefficients (L2 regularization).

• Lasso regression adds a penalty term equivalent to the absolute value of the magnitude of coefficients (L1 regularization).

• Ridge regression tends to shrink the coefficients toward...read more
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Q1. How to delete a table in the database?
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To delete a table in the database, you can use the DROP TABLE statement.
• Use the DROP TABLE statement followed by the table name to delete the table.

• Make sure to backup any important data in the table before deleting it.

• Ensure that you have the necessary permissions to delete the table.
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