Air India Interview Questions, Process, and Tips

Exam maths asked, english, quant asked

In GD they will give you a random topic and gives one minute to discuss about it and you get 30sec to speak about . They see your boldness while speaking and your fluent in English language.

Q1. Self introduction
Ans. First they will ask about self introduction
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Q2. Random questions related to aviation 1
Ans. Then they ask random questions and aviation industry and the history of aviation industry.
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Q3. Random questions about the position you applied
Ans. Then they will ask questions about the position you are applying for.
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Q4. Question about grooming
Ans. Then some random questions like grooming and etc .
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Q5. Question about dressing
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Q6. Question about the behaviour
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Q7. Question about your attitude
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Q8. How much you will rate yourself from 1 to 10
Ans. 
I would rate myself 8 out of 10 for my customer service skills.
• I have extensive experience in handling customer queries and complaints.

• I am empathetic and patient while dealing with customers.

• I have received positive feedback from customers in my previous roles.

• However, there is always room for improvement and I am constantly working on enhancing my skills.
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Data science, ML concepts, statistics, python output

Q1. What does correlation mean? what is the interpretation if the correlation is 0?
Ans. 
Correlation measures the strength and direction of a relationship between two variables. A correlation of 0 indicates no linear relationship.
• Correlation measures the degree to which two variables move in relation to each other. It ranges from -1 to 1.

• A correlation of 0 means there is no linear relationship between the variables. They are not related in a linear fashion.

• For example, if the correlation between hours of s...read more
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Q2. What is collinearity, how to handle it? also for multicollinearity
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Q3. How to evaluate regression models? explain r squared and adjusted r squared and difference between them
Ans. 
Regression models can be evaluated using R squared and adjusted R squared to measure the goodness of fit.
• R squared measures the proportion of the variance in the dependent variable that is predictable from the independent variables.

• Adjusted R squared adjusts for the number of predictors in the model, providing a more accurate measure of goodness of fit.

• R squared can be artificially inflated by adding more predictors, w...read more
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Q4. Explain regularization techniques, difference between ridge and lasso?
Ans. 
Regularization techniques help prevent overfitting in machine learning models. Ridge regression adds L2 regularization, while Lasso regression adds L1 regularization.
• Regularization techniques help prevent overfitting by adding a penalty term to the loss function.

• Ridge regression adds the squared magnitude of coefficients as penalty term (L2 regularization).

• Lasso regression adds the absolute magnitude of coefficients as...read more
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Q5. Explain working of decision trees, how to select parent and child nodes, gini impurity, etc?
Ans. 
Decision trees are a popular machine learning algorithm used for classification and regression tasks.
• Decision trees are a flowchart-like structure where each internal node represents a feature or attribute, each branch represents a decision rule, and each leaf node represents the outcome.

• To select parent and child nodes, the algorithm calculates the best split at each node based on criteria like Gini impurity or inform...read more
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Q6. Bagging and boosting and their difference, what is ensemble models, how to handle overfitting, explain precision recall roc curve
Ans. 
Explanation of bagging, boosting, ensemble models, handling overfitting, and precision-recall-ROC curve.
• Bagging (Bootstrap Aggregating) involves training multiple models on different subsets of the training data and combining their predictions through averaging or voting.

• Boosting involves training multiple models sequentially, with each model correcting the errors of its predecessor.

• Ensemble models combine multiple ind...read more
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Q7. Create dictionary in python from 2 list and key values, sql query for window functions-Rank(), all joins in sql
Ans. 
Creating dictionary in Python from 2 lists, using window functions and joins in SQL
• To create a dictionary in Python from 2 lists and key values, you can use the zip() function

• Example: dict(zip(keys_list, values_list))

• For SQL window functions like Rank(), you can use the OVER() clause

• Example: SELECT column1, column2, RANK() OVER(ORDER BY column3) AS rank_column FROM table_name

• For SQL joins, you can use INNER JOIN, LEFT ...read more
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Q1. To know about the job
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Q2. To know about the job details
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Q1. And to know about the salary
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Q1. If I selected where was my first work place
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Q1. Mostly I want in Chennai
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My name is killi Lakshmi durga. Iam studying inter 2nd year

Q1. What's the job how is it selary
Ans. What's the job how is it selary
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Q2. Which is the city or villages
Ans. Which is the city or villages
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Q3. How many day's in the job
Ans. How many day's in the job
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Q4. What is the best job
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Q5. Which is I am joining day
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Q1. Tell us about yourself
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Q2. How become cabin crew
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