American Airlines Data Engineer Interview Questions and Answers

Q1. Questions about data factory
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Q1. Tell me about a time when you went out of your way to contribute to a project
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Q1. What is the internal implementation of Hashing and what is collision?
Ans. 
Hashing is a technique used to map data to a fixed-size array, collision occurs when two different inputs produce the same hash value.
• Hashing involves using a hash function to map data to a fixed-size array.

• Collision happens when two different inputs produce the same hash value.

• Common techniques to handle collisions include chaining and open addressing.
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Q2. Is time complexity of HashMap always O(1) ?
Ans. 
No, time complexity of HashMap is not always O(1).
• Time complexity of HashMap is O(1) on average, but can degrade to O(n) in worst case scenarios.

• Worst case scenarios include hash collisions, which can lead to linear probing and degrade performance.

• Load factor and capacity of the HashMap can also affect time complexity.

• In scenarios where many keys hash to the same index, the time complexity can degrade.
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Q1. Yet to have the interview. Ambition box is forcefully making me enter these details
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Q1. Explain how did you handle the imbalance in data
Ans. 
I handled data imbalance by using techniques like oversampling, undersampling, SMOTE, or using ensemble methods.
• Used oversampling to increase minority class instances

• Used undersampling to decrease majority class instances

• Applied SMOTE (Synthetic Minority Over-sampling Technique) to generate synthetic samples for minority class

• Utilized ensemble methods like Random Forest or Gradient Boosting to handle imbalance
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Q2. Write a sql query for the given problem statement. Can be solved using advanced concepts like lead and lag
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Advanced sql and basics of data science

Basic coding questions

Q1. Explain in detail about current project? Reason for choosing the algorithm, evaluation metrics and analysis of data
Ans. 
Developed a predictive model for customer churn using logistic regression
• Chose logistic regression for its interpretability and ability to handle binary outcomes

• Evaluated model performance using accuracy, precision, recall, and F1 score

• Analyzed customer data including demographics, purchase history, and customer service interactions
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Q2. Python and sql coding questions - basic
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Q3. Linear regression, xgboost, precision, recall, F1 Score,Correlation
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Q1. About your work experience
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Q1. Tell me about yourself
Ans. 
I am a data analyst with a strong background in statistics and data visualization.
• Graduated with a degree in Statistics

• Proficient in programming languages like Python and R

• Experience in analyzing large datasets and creating data visualizations

• Strong problem-solving skills
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Choose the best answer and question and answer

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Tell me your self and previous company work

Q1. Tell me your self
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Q2. What is the previous role in your previous company
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Q3. What is the your strenght
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1 dsa question 1 sql and 18 mcq questions

Q1. Lot of questions from resumes and projects
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Q2. Easy DSA questions and oops questions
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