Automatic Data Processing (ADP) Data Analyst Intern 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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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
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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. Self-introduction
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Q2. What are your roles and responsibilities in current organization
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I am responsible for analyzing data, creating reports, and providing insights to support decision-making.
• Analyze large datasets to identify trends and patterns

• Create reports and dashboards to visualize data for stakeholders

• Provide insights and recommendations based on data analysis

• Collaborate with cross-functional teams to drive data-driven decision-making
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Q1. Self-introduction
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Q2. Brief discussion about the roles and responsibilities for current opening
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Lead Data Analyst oversees data analysis team, develops strategies, interprets data, and presents insights to stakeholders.
• Manage and lead a team of data analysts

• Develop data analysis strategies and methodologies

• Interpret data and provide actionable insights to stakeholders

• Collaborate with cross-functional teams to drive data-driven decision making

• Ensure data quality and accuracy in analysis

• Stay updated on industry tre
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Q1. Self-introduction
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Q2. Technical round for Python, SQL, Tableau and Advanced Excel
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Q3. Scenario based questions
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Q1. Self-Introduction
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Q2. What do you understand about Sales Comp and its roles and responsibilities
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Sales Comp is a system that determines how salespeople are compensated based on their performance.
• Sales Comp is short for Sales Compensation

• It involves designing and implementing a system to reward salespeople based on their sales performance

• Roles include setting sales targets, determining commission structures, and monitoring sales performance

• Responsibilities include analyzing sales data, calculating commissions, and ...read more
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Q3. Scenario based questions
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Q1. Self-introduction
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Q2. Scenario based questions
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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. Basic pandas questions on dataframes
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Q2. Some quiz questions
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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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