Affine Interview Questions and Answers

Test had a mix of questions on Statistics, Probability, Machine Learning, SQL and Python.

Q1. How to retain special characters (that pandas discards by default) in the data while reading it?
Ans. 
To retain special characters in pandas data, use encoding parameter while reading the data.
• Use encoding parameter while reading the data in pandas

• Specify the encoding type of the data file

• Example: pd.read_csv('filename.csv', encoding='utf-8')
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Q2. How to read large .csv files in pandas quickly?
Ans. 
Use pandas' read_csv() method with appropriate parameters to read large .csv files quickly.
• Use the chunksize parameter to read the file in smaller chunks

• Use the low_memory parameter to optimize memory usage

• Use the dtype parameter to specify data types for columns

• Use the usecols parameter to read only necessary columns

• Use the skiprows parameter to skip unnecessary rows

• Use the nrows parameter to read only a specific numb...read more
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Q3. How do perform the manipulations quicker in pandas?
Ans. 
Use vectorized operations, avoid loops, and optimize memory usage.
• Use vectorized operations like apply(), map(), and applymap() instead of loops.

• Avoid using iterrows() and itertuples() as they are slower than vectorized operations.

• Optimize memory usage by using appropriate data types and dropping unnecessary columns.

• Use inplace=True parameter to modify the DataFrame in place instead of creating a copy.

• Use the pd.eval()...read more
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Q4. Explain generators and decorators in python
Ans. 
Generators are functions that allow you to iterate over a sequence of values without creating the entire sequence in memory. Decorators are functions that modify the behavior of other functions.
• Generators use the yield keyword to return values one at a time

• Generators are memory efficient and can handle large datasets

• Decorators are functions that take another function as input and return a modified version of that funct...read more
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Q5. You have a pandas dataframe with three columns, filled with state names, city names and arbitrary numbers respectively. How to retrieve top 2 cities per state. (top according to the max number in the third...read more
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Q6. How does look up happens in a list when you do my_list[5]?
Ans. 
my_list[5] retrieves the 6th element of the list.
• Indexing starts from 0 in Python.

• The integer inside the square brackets is the index of the element to retrieve.

• If the index is out of range, an IndexError is raised.
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Q7. How to create dictionaries in python with repeated keys?
Ans. 
To create dictionaries in Python with repeated keys, use defaultdict from the collections module.
• Import the collections module

• Create a defaultdict object

• Add key-value pairs to the dictionary using the same key multiple times

• Access the values using the key

• Example: from collections import defaultdict; d = defaultdict(list); d['key'].append('value1'); d['key'].append('value2')
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Q8. What is the purpose of lambda function when regural functions(of def) exist? how are they different?
Ans. 
Lambda functions are anonymous functions used for short and simple operations. They are different from regular functions in their syntax and usage.
• Lambda functions are defined without a name and keyword 'lambda' is used to define them.

• They can take any number of arguments but can only have one expression.

• They are commonly used in functional programming and as arguments to higher-order functions.

• Lambda functions are oft...read more
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Q9. Merge vs join in pandas
Ans. 
Merge and join are used to combine dataframes in pandas.
• Merge is used to combine dataframes based on a common column or index.

• Join is used to combine dataframes based on their index.

• Merge can handle different column names, while join cannot.

• Merge can handle different types of joins (inner, outer, left, right), while join only does inner join by default.
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Q10. How will the resultant table be, when you "merge" two tables that match at a column. and the second table has many of keys repeated.
Ans. 
The resultant table will have all the columns from both tables and the rows will be a combination of matching rows.
• The resultant table will have all the columns from both tables

• The rows in the resultant table will be a combination of matching rows

• If the second table has repeated keys, there will be multiple rows with the same key in the resultant table
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Q11. Some questions on spacy and NLP models and my project.
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Q1. Explain eign vectors and eign values? what purpose do they serve in ML?
Ans. 
Eigenvalues and eigenvectors are linear algebra concepts used in machine learning for dimensionality reduction and feature extraction.
• Eigenvalues represent the scaling factor of the eigenvectors.

• Eigenvectors are the directions along which a linear transformation acts by stretching or compressing.

• In machine learning, eigenvectors are used for principal component analysis (PCA) to reduce the dimensionality of data.

• Eigenv...read more
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Q2. Explain PCA briefly? what can it be used for and what can it not be used for?
Ans. 
PCA is a dimensionality reduction technique used to transform high-dimensional data into a lower-dimensional space.
• PCA can be used for feature extraction, data visualization, and noise reduction.

• PCA cannot be used for causal inference or to handle missing data.

• PCA assumes linear relationships between variables and may not work well with non-linear data.

• PCA can be applied to various fields such as finance, image process...read more
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Q3. What is VIF and how is it calculated?
Ans. 
VIF stands for Variance Inflation Factor, a measure of multicollinearity in regression analysis.
• VIF is calculated for each predictor variable in a regression model.

• It measures how much the variance of the estimated regression coefficient is increased due to multicollinearity.

• A VIF of 1 indicates no multicollinearity, while a VIF greater than 1 indicates increasing levels of multicollinearity.

• VIF is calculated as 1 / (1...read more
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Q4. What is AIC & BIC in linear regression?
Ans. 
AIC & BIC are statistical measures used to evaluate the goodness of fit of a linear regression model.
• AIC stands for Akaike Information Criterion and BIC stands for Bayesian Information Criterion.

• Both AIC and BIC are used to compare different models and select the best one.

• AIC penalizes complex models less severely than BIC.

• Lower AIC/BIC values indicate a better fit of the model to the data.

• AIC and BIC can be calculated...read more
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Q5. Do we minimize or maximize the loss in logistic regression?
Ans. 
We minimize the loss in logistic regression.
• The goal of logistic regression is to minimize the loss function.

• The loss function measures the difference between predicted and actual values.

• The optimization algorithm tries to find the values of coefficients that minimize the loss function.

• Minimizing the loss function leads to better model performance.

• Examples of loss functions used in logistic regression are cross-entropy...read more
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Q6. How does one vs rest work for logistic regression?
Ans. 
One vs Rest is a technique used to extend binary classification to multi-class problems in logistic regression.
• It involves training multiple binary classifiers, one for each class.

• In each classifier, one class is treated as the positive class and the rest as negative.

• The class with the highest probability is predicted as the final output.

• It is also known as one vs all or one vs others.

• Example: In a 3-class problem, we ...read more
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Q7. What is one vs one classification?
Ans. 
One vs one classification is a binary classification method where multiple models are trained to classify each pair of classes.
• It is used when there are more than two classes in the dataset.

• It involves training multiple binary classifiers for each pair of classes.

• The final prediction is made by combining the results of all the binary classifiers.

• Example: In a dataset with 5 classes, 10 binary classifiers will be traine...read more
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Q8. How to find the number of white cars in a city? (interviewer wanted my approach and had given me 5 minutes to come up with an apporach)
Ans. 
Estimate the number of white cars using surveys, traffic data, and image recognition techniques.
• Conduct surveys: Ask residents about car colors in their neighborhoods.

• Use traffic cameras: Analyze footage to count white cars during peak hours.

• Leverage social media: Analyze posts or images of cars in the city.

• Utilize machine learning: Train a model on images of cars to identify white ones.

• Collaborate with local authoriti...read more
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Q1. All employees having same salary in the smae department sql and pysprk
Ans. 
Identify employees with the same salary within the same department using SQL and PySpark.
• Use SQL's GROUP BY clause to group employees by department and salary.

• Example SQL query: SELECT department, salary FROM employees GROUP BY department, salary HAVING COUNT(*) > 1;

• In PySpark, use DataFrame operations to group by department and salary.

• Example PySpark code: df.groupBy('department', 'salary').count().filter('count &g...read more
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Q2. How to create pipeline in databricks
Ans. 
To create a pipeline in Databricks, you can use Databricks Jobs or Apache Airflow for orchestration.
• Use Databricks Jobs to create a pipeline by scheduling notebooks or Spark jobs.

• Utilize Apache Airflow for more complex pipeline orchestration with dependencies and monitoring.

• Leverage Databricks Delta for managing data pipelines with ACID transactions and versioning.
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Q3. Palindrome, 2nd char in every word make is to upper case, sql rank and dense rank releated questions , given 2 tables country and city we need to calculate total population in each continent by joining the...read more
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Q4. String manuplation questions inpython
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A coding test was administered to address several machine learning problem statements.

Q1. Mention some metric in ML and explain the tradeoffs between them
Ans. 
ML metrics help evaluate model performance, each with trade-offs affecting accuracy, interpretability, and application.
• Accuracy vs. Precision: High accuracy may come with low precision in imbalanced datasets. Example: Classifying rare diseases.

• Recall vs. F1 Score: High recall may lower F1 score, impacting balance in precision and recall. Example: Fraud detection.

• ROC-AUC vs. PR-AUC: ROC-AUC is sensitive to class imbalan...read more
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Q2. How does the xgboost algorithm works , with explanation on every steps
Ans. 
XGBoost is an efficient implementation of gradient boosting that optimizes performance and accuracy through ensemble learning.
• 1. **Gradient Boosting Framework**: XGBoost builds models in a sequential manner, where each new model corrects errors made by the previous ones.

• 2. **Decision Trees**: It primarily uses decision trees as base learners, where each tree is built to minimize the loss function.

• 3. **Regularization**:...read more
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Q1. Write a program to reverse an array of integers and strings?
Ans. 
This program reverses an array containing both integers and strings.
• Use a loop to iterate through the array from the last index to the first.

• Create a new array to store the reversed elements.

• Example: For input ['apple', 1, 'banana', 2], output should be [2, 'banana', 1, 'apple'].
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Q2. Write a program to count the maximum repetitive integer among one array?
Ans. 
This program counts the most frequently occurring integer in an array, identifying the maximum repetitive integer efficiently.
• Use a dictionary to store the count of each integer in the array. For example, for the array [1, 2, 2, 3], the counts would be {1: 1, 2: 2, 3: 1}.

• Iterate through the array and update the count in the dictionary for each integer encountered.

• After counting, find the integer with the maximum count....read more
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Q3. Project discussion and why use specific algorithm
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Q4. How does linear programming algorithm works ?
Ans. 
Linear programming optimizes a linear objective function subject to linear constraints, finding the best outcome in a feasible region.
• Linear programming involves maximizing or minimizing a linear objective function.

• Constraints are linear inequalities that define the feasible region.

• The feasible region is typically a convex polygon in multi-dimensional space.

• The Simplex method is a popular algorithm used to solve linear...read more
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Q1. Questions from SQL(40 Mins), Power BI (25 Mins) and Excel(15 Mins)
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Q2. Joins Number of records in inner outer left right joins, SQL Query and foundational questions, DAX Writing for Running total sales
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