Wells Fargo Data Management Analyst Interview Questions and Answers

Q1. Types of Chunking in data preparation in RAG
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Q2. How Embedding works in Vector Databases
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Q3. Explain ARIMA model
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Q4. How can we decide to choose Linear Regression for a business problem
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Q1. What is token and it's limit for Open Source LLMs
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Q2. Difference of a Regression and Time Series problem
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Q3. Advantage of LSTM over RNN
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Q4. Performance Metrics for Logistic Regression
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Basic aptitude questions

Data structure and algorithms

Q1. Java, SQL questions
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Q1. Casual talk about roles
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Q1. What is p value?
Ans. 
A p-value is a measure used in statistical hypothesis testing to determine the strength of evidence against the null hypothesis.
• A p-value is the probability of obtaining results as extreme as the observed results, assuming the null hypothesis is true.

• A p-value is compared to a significance level (usually 0.05) to determine if the null hypothesis should be rejected.

• A p-value less than the significance level indicates st
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Q2. Tell the output a**2
Ans. 
The output of a**2 is the square of the value of a.
• The output is the value of a multiplied by itself

• For example, if a = 3, then the output would be 9 (3*3)
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Q3. Difference between append() and concat
Ans. 
append() adds elements to a single DataFrame, while concat() combines multiple DataFrames.
• append() is a method used to add rows to a DataFrame.

• concat() is a function used to combine multiple DataFrames along a particular axis.

• append() modifies the original DataFrame, while concat() returns a new DataFrame.

• Example: df1.append(df2) vs pd.concat([df1, df2])
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Q1. Explain error handling in PySpark
Ans. 
Error handling in PySpark involves using try-except blocks and logging to handle exceptions and errors.
• Use try-except blocks to catch and handle exceptions in PySpark code

• Utilize logging to record errors and exceptions for debugging purposes

• Consider using the .option('mode', 'PERMISSIVE') method to handle corrupt records in data processing
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Q1. Data Warehousing related questions
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Q1. Data Modelling related questions
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Q1. Advanced sql on ctl
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Q2. Python data structure
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Q1. Asked about skillsets & tools used in the current job. I was asked basic questions on SQL & Tableau
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Detailed interview on SQL, Tableau & Alteryx

Q1. Money you want for this position
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Q2. Why to choose this position and not another
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