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Agility E Services Data Scientist Interview Questions and Answers

Updated 26 Aug 2017

Agility E Services Data Scientist Interview Experiences

2 interviews found

I was interviewed in Apr 2017.

Interview Questionnaire 

2 Questions

  • Q1. Past and current projects work, and few basic machine learning concepts
  • Q2. Questions about machine learning concepts, E.g. classification, regression, clustering, over fitting vs under fitting, boosting etc.

Interview Preparation Tips

Round: Technical Interview
Experience: It was telephonic interview, started with introduction, asked me about projects, technologies.
Tips: Know your projects work in detail.

Round: Technical Interview
Experience: It was video conferencing. Interviewer was very informal that made me relaxed and comfortable, asked me various questions on data science, I tried to answer those with how I used those concept in my projects work. It ended on positive note.
Tips: Be truthful, don't assume anything.

College Name: IIT Guwahati

Interview Questionnaire 

1 Question

  • Q1. Questions about current and past projects and machine learning concepts.

Interview Preparation Tips

Round: Technical Interview
Experience: It was telephonic interview, started with introduction then interviewer asked me about current project and technologies using for implementation. Interviewer was very calm and friendly so I was relaxed, in the end we discussed about job profile, work environment and their expectation from me.
Tips: Know your projects work in details, answer to asked questions very brief and precise, if you don't know answer then be truthfull about it.

College Name: IIT Guwahati

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Interview questions from similar companies

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
5
Excellent
Difficulty level
Easy
Process Duration
Less than 2 weeks
Result
Not Selected

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

Round 1 - One-on-one 

(3 Questions)

  • Q1. Tell me about yourself?
  • Ans. 

    I am a data scientist with a background in statistics and machine learning, passionate about solving complex problems using data-driven approaches.

    • Background in statistics and machine learning

    • Experience in solving complex problems using data-driven approaches

    • Passionate about leveraging data to drive insights and decision-making

  • Answered by AI
  • Q2. Describe in detail about one of my main project.
  • Ans. 

    Developed a predictive model for 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.

    • Implemented the model into the company's CRM system for real-time predictions.

  • Answered by AI
  • Q3. Few questions related to my projects.
Round 2 - Technical 

(1 Question)

  • Q1. Questions on Basics python(Since i am fresher)

Interview Preparation Tips

Interview preparation tips for other job seekers - Overall, it was a good experience for me. Very friendly interviewers. I couldn't make it after the second round. I came to know where I was lacking.
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

Interview experience
5
Excellent
Difficulty level
-
Process Duration
Less than 2 weeks
Result
-

I was interviewed in Aug 2024.

Round 1 - Technical 

(2 Questions)

  • Q1. DFA Focus :Sorting ,Searching ,Stacks,Queues, HashMaps
  • Q2. Os & cn: Process scheduling, TCP/IP, HTTP basics
Interview experience
1
Bad
Difficulty level
Moderate
Process Duration
-
Result
No response

I was interviewed in May 2024.

Round 1 - One-on-one 

(2 Questions)

  • Q1. Tell me about your self?
  • Q2. What is maths and stats
  • Ans. 

    Maths and stats refer to the study of mathematical concepts and statistical methods for analyzing data.

    • Maths involves the study of numbers, quantities, shapes, and patterns.

    • Stats involves collecting, analyzing, interpreting, and presenting data.

    • Maths is used to solve equations, calculate probabilities, and model real-world phenomena.

    • Stats is used to make informed decisions, draw conclusions, and test hypotheses.

    • Both ma...

  • Answered by AI
Round 2 - Coding Test 

Confusion matrix what are your job rolls explain me Gradient boosting algorithm?

Interview Preparation Tips

Interview preparation tips for other job seekers - Be very serious on every answer
Interview experience
3
Average
Difficulty level
-
Process Duration
-
Result
-
Round 1 - Technical 

(2 Questions)

  • Q1. Number of Duplicate words in a string
  • Ans. 

    Count the number of duplicate words in a string.

    • Split the string into words using a delimiter like space or punctuation.

    • Create a dictionary to store the count of each word.

    • Iterate through the words and increment the count in the dictionary.

    • Count the number of words with count greater than 1 as duplicates.

  • Answered by AI
  • Q2. Chunking in LLM
  • Ans. 

    Chunking in LLM refers to breaking down text into smaller chunks for better processing by the language model.

    • Chunking helps improve the efficiency of the language model by breaking down large text inputs into smaller segments.

    • It can help the model better understand the context and relationships within the text.

    • Chunking is commonly used in natural language processing tasks such as text summarization and sentiment analys

  • Answered by AI

Skills evaluated in this interview

Interview experience
2
Poor
Difficulty level
-
Process Duration
-
Result
-
Round 1 - Technical 

(1 Question)

  • Q1. Machine learning related questions and the theory of its operation
Interview experience
4
Good
Difficulty level
-
Process Duration
-
Result
-
Round 1 - Coding Test 

Expect technical questions as well as moderate level coding questions

Round 2 - Technical 

(1 Question)

  • Q1. Data science and MLOps concepts
Round 3 - HR 

(1 Question)

  • Q1. Behavioral and managerial rounds

Agility E Services Interview FAQs

How to prepare for Agility E Services 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 Agility E Services. The most common topics and skills that interviewers at Agility E Services expect are Artificial Intelligence, Machine Learning and Power Bi.
What are the top questions asked in Agility E Services Data Scientist interview?

Some of the top questions asked at the Agility E Services Data Scientist interview -

  1. Questions about machine learning concepts, E.g. classification, regression, clu...read more
  2. Past and current projects work, and few basic machine learning conce...read more
  3. Questions about current and past projects and machine learning concep...read more

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