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Amantya Technologies Data Scientist Interview Questions and Answers

Updated 11 May 2021

Amantya Technologies Data Scientist Interview Experiences

1 interview found

I applied via Company Website and was interviewed before May 2020. There were 3 interview rounds.

Interview Questionnaire 

1 Question

  • Q1. Question asked were on the fundamentals of selected Classification and Regression algorithms, Live coding(Basic and Easy) , Basic Knowledge on any cloud service, Deployment of ML projects on cloud .

Interview Preparation Tips

Interview preparation tips for other job seekers - Prepare the math and fundamentals behind each ML or DL algorithms. No need to know all State of the Art algorithms , be sure on whatever you know. Deployment using Flask and on any cloud service is required. Strong grip on python like using basic data structures like using Dictionaries, set, lists and other functions like lambda,filter, map etc.

Interview questions from similar companies

I applied via Job Portal and was interviewed in Mar 2022. There were 2 interview rounds.

Round 1 - Coding Test 

(1 Question)

  • Q1. Machine Learning, Python, SQL, Basic Stats The difficulty level of questions was average.
Round 2 - Technical 

(1 Question)

  • Q1. Resume and project related. One or two questions around probability and stats.

Interview Preparation Tips

Interview preparation tips for other job seekers - Round 1 have MCQ questions around machine Learning , data science and sql with average difficulty
Round 2 -- Technical round. Asked mostly around resume and the project mentioned.
Also asked to do live python and sql coding

I applied via Naukri.com and was interviewed in Feb 2022. There were 4 interview rounds.

Round 1 - Resume Shortlist 
Pro Tip by AmbitionBox:
Keep your resume crisp and to the point. A recruiter looks at your resume for an average of 6 seconds, make sure to leave the best impression.
View all tips
Round 2 - Coding Test 

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

Round 3 - Technical 

(11 Questions)

  • 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')

  • Answered by AI
  • 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...

  • Answered by AI
  • 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()...

  • Answered by AI
  • 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...

  • Answered by AI
  • 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...
  • Ans. 

    Retrieve top 2 cities per state based on max number in third column of pandas dataframe.

    • Group the dataframe by state column

    • Sort each group by the third column in descending order

    • Retrieve the top 2 rows of each group using head(2) function

    • Concatenate the resulting dataframes using pd.concat() function

  • Answered by AI
  • 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.

  • Answered by AI
  • 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')

  • Answered by AI
  • 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...

  • Answered by AI
  • 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.

  • Answered by AI
  • 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

  • Answered by AI
  • Q11. Some questions on spacy and NLP models and my project.
Round 4 - Technical 

(8 Questions)

  • 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...

  • Answered by AI
  • 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

  • Answered by AI
  • 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...

  • Answered by AI
  • 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...

  • Answered by AI
  • 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

  • Answered by AI
  • 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 ...

  • Answered by AI
  • 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

  • Answered by AI
  • 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)

Interview Preparation Tips

Interview preparation tips for other job seekers - for the most part, practical questions were asked. so, your experience would matter the most. hence prepare accordingly.

Skills evaluated in this interview

Interview experience
5
Excellent
Difficulty level
Moderate
Process Duration
Less than 2 weeks
Result
Not Selected

I applied via Walk-in and was interviewed before Oct 2023. There was 1 interview round.

Round 1 - Technical 

(1 Question)

  • Q1. Proficiency in cloud technology
  • Ans. 

    Proficiency in cloud technology is essential for data scientists to efficiently store, manage, and analyze large datasets.

    • Experience with cloud platforms like AWS, Azure, or Google Cloud

    • Knowledge of cloud storage solutions like S3, Blob Storage, or Cloud Storage

    • Understanding of cloud computing concepts like virtual machines, containers, and serverless computing

    • Ability to work with big data technologies like Hadoop, Spa

  • Answered by AI

Interview Preparation Tips

Interview preparation tips for other job seekers - Be honest and true about yourself

Skills evaluated in this interview

Interview experience
1
Bad
Difficulty level
Easy
Process Duration
Less than 2 weeks
Result
Not Selected

I applied via Referral and was interviewed in Mar 2024. There was 1 interview round.

Round 1 - Technical 

(1 Question)

  • Q1. Questions based on my work experience, basic level of python coding (return counts for duplicate integers in a list), theory questions on basic DL (activation layers, time series)

Interview Preparation Tips

Interview preparation tips for other job seekers - Do not get upset if you fail interviews, many of these end up hiring internally or are simply following the protocol of interviewing you after already having selected some other candidate. Just keep on trying!

Data Scientist Interview Questions & Answers

Affine user image Dheeraj Warudkar

posted on 8 Jul 2024

Interview experience
4
Good
Difficulty level
-
Process Duration
-
Result
-
Round 1 - Technical 

(1 Question)

  • Q1. Machine learning
Round 2 - Coding Test 

Python , pandas, sql

Interview experience
5
Excellent
Difficulty level
-
Process Duration
-
Result
-
Round 1 - Technical 

(1 Question)

  • Q1. What is r squared value
  • Ans. 

    R-squared value is a statistical measure that represents the proportion of the variance in the dependent variable that is predictable from the independent variable(s).

    • R-squared value ranges from 0 to 1, with 1 indicating a perfect fit.

    • It is used to evaluate the goodness of fit of a regression model.

    • A higher R-squared value indicates that the model explains a larger proportion of the variance in the dependent variable.

    • F...

  • Answered by AI
Interview experience
5
Excellent
Difficulty level
-
Process Duration
-
Result
-
Round 1 - Aptitude Test 

Basic aptitude questions, statics, probability etc

Round 2 - HR 

(2 Questions)

  • Q1. Where do you live in the USA
  • Ans. 

    I live in New York City, the bustling metropolis known for its iconic landmarks and diverse culture.

    • I reside in the heart of Manhattan, surrounded by towering skyscrapers.

    • New York City is home to famous attractions like Times Square, Central Park, and the Statue of Liberty.

    • The city offers a vibrant arts and entertainment scene, with Broadway shows and world-class museums.

    • I enjoy exploring the diverse neighborhoods and ...

  • Answered by AI
  • Q2. Where do you see yourself in 5 years
  • Ans. 

    In 5 years, I see myself as a senior data analyst leading a team, utilizing advanced analytics to drive strategic decision-making.

    • Leading a team of data analysts

    • Utilizing advanced analytics techniques

    • Driving strategic decision-making

    • Continuously learning and staying updated with the latest trends in data analysis

  • Answered by AI

Interview Preparation Tips

Interview preparation tips for other job seekers - Basic aptitude and math skills are the key to success in the field of psychology and psychology in the field and in the field in
Interview experience
5
Excellent
Difficulty level
Moderate
Process Duration
-
Result
Not Selected

I applied via Naukri.com and was interviewed in Jan 2024. There was 1 interview round.

Round 1 - Technical 

(1 Question)

  • Q1. Difference between Primary Key and Unique Key ?
  • Ans. 

    Primary key uniquely identifies each record in a table, while Unique key allows only unique values but can have null values.

    • Primary key enforces uniqueness and not null constraint on a column

    • Primary key can consist of multiple columns

    • Unique key allows only unique values but can have null values

    • Unique key can be applied to multiple columns as well

  • Answered by AI
Interview experience
3
Average
Difficulty level
Moderate
Process Duration
-
Result
-

I applied via Approached by Company

Round 1 - Technical 

(2 Questions)

  • Q1. Sql is the most important , Focus on Joins ,Sub query and Sql basics
  • Q2. Joining tables with different joins
  • Ans. 

    Joining tables with different joins in SQL

    • Use INNER JOIN to return rows when there is at least one match in both tables

    • Use LEFT JOIN to return all rows from the left table and the matched rows from the right table

    • Use RIGHT JOIN to return all rows from the right table and the matched rows from the left table

    • Use FULL JOIN to return rows when there is a match in one of the tables

  • Answered by AI

Skills evaluated in this interview

Amantya Technologies Interview FAQs

How to prepare for Amantya Technologies Data Scientist interview?
Go through your CV in detail and study all the technologies mentioned in your CV. Prepare at least two technologies or languages in depth if you are appearing for a technical interview at Amantya Technologies. The most common topics and skills that interviewers at Amantya Technologies expect are Teradata, Analytics, Data Analysis, Data Processing and Forecasting.

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