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Fego Associate Data Scientist Interview Questions and Answers

Updated 4 Nov 2024

Fego Associate Data Scientist Interview Experiences

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Associate Data Scientist Interview Questions & Answers

user image Kranthi Kiran Reddy

posted on 4 Nov 2024

Interview experience
4
Good
Difficulty level
Moderate
Process Duration
Less than 2 weeks
Result
Selected Selected

I was interviewed before Nov 2023.

Round 1 - Technical 

(3 Questions)

  • Q1. What are RNNs ?
  • Ans. 

    RNNs are Recurrent Neural Networks, a type of neural network designed to handle sequential data by retaining memory of previous inputs.

    • RNNs have loops that allow information to persist, making them suitable for tasks like speech recognition and language modeling.

    • They can process inputs of varying lengths and are capable of learning patterns in sequences.

    • Examples of RNN variants include LSTM (Long Short-Term Memory) and

  • Answered by AI
  • Q2. Explain GRUs and LSTMs
  • Ans. 

    GRUs and LSTMs are types of recurrent neural networks (RNNs) used for sequential data processing.

    • GRUs (Gated Recurrent Units) are a simplified version of LSTMs with fewer gates.

    • LSTMs (Long Short-Term Memory) are a type of RNN that can learn long-term dependencies.

    • Both GRUs and LSTMs are designed to address the vanishing gradient problem in traditional RNNs.

    • GRUs have reset and update gates, while LSTMs have input, outpu...

  • Answered by AI
  • Q3. Explain about Gradient Decay
  • Ans. 

    Gradient decay is a technique used in optimization algorithms to gradually reduce the learning rate over time.

    • Gradient decay helps prevent overshooting the minimum point in the loss function by slowing down the learning rate as the optimization process progresses.

    • Common methods of gradient decay include exponential decay, polynomial decay, and step decay.

    • Exponential decay reduces the learning rate exponentially over ti...

  • Answered by AI
Round 2 - Case Study 

How to improve the ETA estimation for Food delivery apps and how to improve customer retention and engagment

Skills evaluated in this interview

Interview questions from similar companies

Interview experience
4
Good
Difficulty level
Moderate
Process Duration
2-4 weeks
Result
Selected Selected

I applied via Referral and was interviewed in Apr 2023. 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 - Technical 

(1 Question)

  • Q1. Basic Data science related questions on Overfitting, classification models.
Round 3 - Technical 

(1 Question)

  • Q1. It was managerial round, More about how you will handle work and questions on project.
Round 4 - HR 

(1 Question)

  • Q1. Salary negotiation

I applied via Naukri.com and was interviewed in Nov 2020. There was 1 interview round.

Interview Questionnaire 

4 Questions

  • Q1. Tell me about yourself
  • Q2. Why Amazon?
  • Q3. What do u know about Alexa?
  • Q4. What would u like to see yourself in 5 years from now?

Interview Preparation Tips

Interview preparation tips for other job seekers - It was online interview through video calling. Whatever may be the questions our answers should be natural and self made. You can refer HR interview QA but prepare answers for the most common questions by yourself .
Interview experience
4
Good
Difficulty level
Moderate
Process Duration
2-4 weeks
Result
Selected Selected

I applied via Campus Placement and was interviewed in Nov 2024. There were 3 interview rounds.

Round 1 - Aptitude Test 

There were verbal, non verbal, reasoning , English and maths questions

Round 2 - Technical 

(2 Questions)

  • Q1. Tell me about your project.
  • Ans. 

    I worked on a project analyzing customer behavior using machine learning algorithms.

    • Used Python for data preprocessing and analysis

    • Implemented machine learning models such as decision trees and logistic regression

    • Performed feature engineering to improve model performance

  • Answered by AI
  • Q2. What programming knowledge you have ?
  • Ans. 

    Proficient in Python, R, and SQL with experience in data manipulation, visualization, and machine learning algorithms.

    • Proficient in Python for data analysis and machine learning tasks

    • Experience with R for statistical analysis and visualization

    • Knowledge of SQL for querying databases and extracting data

    • Familiarity with libraries such as Pandas, NumPy, Matplotlib, and Scikit-learn

  • Answered by AI
Round 3 - HR 

(2 Questions)

  • Q1. Where do you stay ?
  • Ans. 

    I currently stay in an apartment in downtown area.

    • I stay in an apartment in downtown area

    • My current residence is in a city

    • I live close to my workplace

  • Answered by AI
  • Q2. Tell me about you
  • Ans. 

    I am a data science enthusiast with a strong background in statistics and machine learning.

    • Background in statistics and machine learning

    • Passionate about data science

    • Experience with data analysis tools like Python and R

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

(3 Questions)

  • Q1. Difference between bagging and boosting
  • Ans. 

    Bagging and boosting are ensemble learning techniques used to improve the performance of machine learning models by combining multiple weak learners.

    • Bagging (Bootstrap Aggregating) involves training multiple models independently on different subsets of the training data and then combining their predictions through averaging or voting.

    • Boosting involves training multiple models sequentially, where each subsequent model c...

  • Answered by AI
  • Q2. Parameters of Decision Tree
  • Ans. 

    Parameters of a Decision Tree include max depth, min samples split, criterion, and splitter.

    • Max depth: maximum depth of the tree

    • Min samples split: minimum number of samples required to split an internal node

    • Criterion: function to measure the quality of a split (e.g. 'gini' or 'entropy')

    • Splitter: strategy used to choose the split at each node (e.g. 'best' or 'random')

  • Answered by AI
  • Q3. Explain any one of your project in detail
  • Ans. 

    Developed a predictive model to forecast customer churn in a telecom company

    • Collected and cleaned customer data including usage patterns and demographics

    • Used machine learning algorithms such as logistic regression and random forest to build the model

    • Evaluated model performance using metrics like accuracy, precision, and recall

    • Provided actionable insights to the company to reduce customer churn rate

  • Answered by AI

Skills evaluated in this interview

Interview experience
3
Average
Difficulty level
Easy
Process Duration
Less than 2 weeks
Result
Not Selected

I was interviewed in Oct 2024.

Round 1 - Technical 

(1 Question)

  • Q1. Project related questions from your CV
Round 2 - Technical 

(2 Questions)

  • Q1. Question on transformers
  • Q2. Comparison of transfer learning and fintuning.
  • Ans. 

    Transfer learning involves using pre-trained models on a different task, while fine-tuning involves further training a pre-trained model on a specific task.

    • Transfer learning uses knowledge gained from one task to improve learning on a different task.

    • Fine-tuning involves adjusting the parameters of a pre-trained model to better fit a specific task.

    • Transfer learning is faster and requires less data compared to training a...

  • Answered by AI

Skills evaluated in this interview

Interview experience
3
Average
Difficulty level
Moderate
Process Duration
Less than 2 weeks
Result
Selected Selected

I applied via Approached by Company and was interviewed in Aug 2024. There were 2 interview rounds.

Round 1 - Coding Test 

*****, arjumpudi satyanarayana

Round 2 - Technical 

(5 Questions)

  • Q1. What is the python language
  • Ans. 

    Python is a high-level programming language known for its simplicity and readability.

    • Python is widely used for web development, data analysis, artificial intelligence, and scientific computing.

    • It emphasizes code readability and uses indentation for block delimiters.

    • Python has a large standard library and a vibrant community of developers.

    • Example: print('Hello, World!')

    • Example: import pandas as pd

  • Answered by AI
  • Q2. What is the code problems
  • Ans. 

    Code problems refer to issues or errors in the code that need to be identified and fixed.

    • Code problems can include syntax errors, logical errors, or performance issues.

    • Examples of code problems include missing semicolons, incorrect variable assignments, or inefficient algorithms.

    • Identifying and resolving code problems is a key skill for data scientists to ensure accurate and efficient data analysis.

  • Answered by AI
  • Q3. What is the python code
  • Ans. 

    Python code is a programming language used for data analysis, machine learning, and scientific computing.

    • Python code is written in a text editor or an integrated development environment (IDE)

    • Python code is executed using a Python interpreter

    • Python code can be used for data manipulation, visualization, and modeling

  • Answered by AI
  • Q4. What is the project
  • Ans. 

    The project is a machine learning model to predict customer churn for a telecommunications company.

    • Developing predictive models using machine learning algorithms

    • Analyzing customer data to identify patterns and trends

    • Evaluating model performance and making recommendations for reducing customer churn

  • Answered by AI
  • Q5. What is the lnderssip
  • Ans. 

    The question seems to be incomplete or misspelled.

    • It is possible that the interviewer made a mistake while asking the question.

    • Ask for clarification or context to provide a relevant answer.

  • Answered by AI

Interview Preparation Tips

Topics to prepare for IBM Data Scientist interview:
  • Python
  • Machine Learning
Interview preparation tips for other job seekers - No

Skills evaluated in this interview

Interview experience
3
Average
Difficulty level
Moderate
Process Duration
Less than 2 weeks
Result
Not Selected

I applied via Approached by Company and was interviewed in Sep 2024. There was 1 interview round.

Round 1 - Technical 

(2 Questions)

  • Q1. Gave one easy question and asked what will be the output
  • Q2. Leetcode 2 sum question

Interview Preparation Tips

Interview preparation tips for other job seekers - I was pretty much sure that I would pass L1 round and hoping for L2 round. I was interviewing for Generative AI Engineer. It was full 1 hr. The interviewer was less experienced than me. He asked me about my current work and focused more on previous work. I gave 80% correct answers and still did not make it. Don't know what they were expecting from me. Then I thought, maybe they are just taking the interview for the name sake. Sometimes, rejections are baseless.
Interview experience
3
Average
Difficulty level
Moderate
Process Duration
4-6 weeks
Result
Not Selected

I applied via Naukri.com and was interviewed in Sep 2024. There were 2 interview rounds.

Round 1 - Technical 

(3 Questions)

  • Q1. Overfitting and Underfitting
  • Q2. Find Nth-largest element
  • Ans. 

    Find Nth-largest element in an array

    • Sort the array in descending order

    • Return the element at index N-1

  • Answered by AI
  • Q3. NLP Data preprocessing
Round 2 - HR 

(2 Questions)

  • Q1. Salary Discussion
  • Q2. Fitment discussion

Skills evaluated in this interview

Interview experience
4
Good
Difficulty level
Moderate
Process Duration
-
Result
No response

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

Round 1 - Technical 

(6 Questions)

  • Q1. Which GenAI projects I have worked on
  • Q2. What is the context window in LLMs
  • Ans. 

    Context window in LLMs refers to the number of surrounding words considered when predicting the next word in a sequence.

    • Context window helps LLMs capture dependencies between words in a sentence.

    • A larger context window allows the model to consider more context but may lead to increased computational complexity.

    • For example, in a context window of 2, the model considers 2 words before and 2 words after the target word fo

  • Answered by AI
  • Q3. What is top_k parameter
  • Ans. 

    top_k parameter is used to specify the number of top elements to be returned in a result set.

    • top_k parameter is commonly used in machine learning algorithms to limit the number of predictions or recommendations.

    • For example, in recommendation systems, setting top_k=5 will return the top 5 recommended items for a user.

    • In natural language processing tasks, top_k can be used to limit the number of possible next words in a

  • Answered by AI
  • Q4. What are regex patterns in python
  • Ans. 

    Regex patterns in Python are sequences of characters that define a search pattern.

    • Regex patterns are used for pattern matching and searching in strings.

    • They are created using the 're' module in Python.

    • Examples of regex patterns include searching for email addresses, phone numbers, or specific words in a text.

  • Answered by AI
  • Q5. What are iterators and tuples
  • Ans. 

    Iterators are objects that allow iteration over a sequence of elements. Tuples are immutable sequences of elements.

    • Iterators are used to loop through elements in a collection, like lists or dictionaries

    • Tuples are similar to lists but are immutable, meaning their elements cannot be changed

    • Example of iterator: for item in list: print(item)

    • Example of tuple: my_tuple = (1, 2, 3)

  • Answered by AI
  • Q6. Do I have REST API experience
  • Ans. 

    Yes, I have experience working with REST APIs in various projects.

    • Developed RESTful APIs using Python Flask framework

    • Consumed REST APIs in data analysis projects using requests library

    • Used Postman for testing and debugging REST APIs

  • Answered by AI

Skills evaluated in this interview

Fego Interview FAQs

How many rounds are there in Fego Associate Data Scientist interview?
Fego interview process usually has 2 rounds. The most common rounds in the Fego interview process are Technical and Case Study.
What are the top questions asked in Fego Associate Data Scientist interview?

Some of the top questions asked at the Fego Associate Data Scientist interview -

  1. What are RNN...read more
  2. Explain GRUs and LS...read more
  3. Explain about Gradient De...read more

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