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Intellect Design Arena Data Scientist Interview Questions and Answers
Intellect Design Arena Data Scientist Interview Experiences
4 interviews found
Anonymous
Selected
I applied via Campus Placement and was interviewed in Nov 2023. There was 1 interview round.
(1 Question)
- Q1. Questions related to the projects in the resume and some questions on machine learning concepts.
Krishna Sai
Selected
I applied via LinkedIn and was interviewed before Aug 2023. There were 4 interview rounds.
(2 Questions)
- Q1. About the Projects I was working on and step by step detailing
- Q2. Since it is NLP related projects mostly on Embeddings and word related questions
Its a take-home assignment related to NLP multi-class classification
(2 Questions)
- Q1. Again they discussed my insights on the projects
- Q2. Some case studies and how we can solve it
(2 Questions)
- Q1. Salary Negotiation
- Q2. Accepting offer
I applied via Naukri.com and was interviewed in Jun 2022. There were 3 interview rounds.
(3 Questions)
- Q1. What is tokenization in NLP? and, to get raw tokens for a sentence with words seperated by space, why use tokenizers from nltk instead of str.split()?
- Ans.
Tokenization in NLP is the process of breaking down text into smaller units called tokens.
Tokenization is a fundamental step in NLP for text preprocessing.
Tokens can be words, phrases, or even individual characters.
Tokenization helps in preparing text data for further analysis or modeling.
NLTK tokenizers provide additional functionalities like handling contractions, punctuation, etc.
str.split() may not handle complex t...
- Q2. While tokenzing, if you want to not break some word pairs(or triplets), for ex, to not tokenize the words 'first' and 'name' when they occur together and consider them as a single token, how would you do i...
- Q3. For a data with 1000 samples and 700 dimensions, how would you find a line that best fits the data, to be able to extrapolate? this is not a supervised ML problem, there's no target. and how would you do i...
- Ans.
To find a line that best fits the data with 1000 samples and 700 dimensions, we can use linear regression.
For unsupervised ML approach, we can use Principal Component Analysis (PCA) to reduce dimensions and then fit a line using linear regression.
For supervised ML approach, we need to select a target column. We can choose any of the 700 dimensions as the target and treat it as a regression problem.
Potential problems of...
(3 Questions)
- Q1. This round was entirely based on my projects on the resume!!!!
- Q2. Explain project starting from the business problem to the deployment? [I was being interrupted for clarifications and questions]
- Q3. [all of the follow up questions were baed on my answers] (some questions on businesss problem and rationale behing it, end users, how success will be measured and data collection that I can't disclose) a...
Interview Preparation Tips
Skills evaluated in this interview
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amruta chothave
Selected
I applied via Naukri.com and was interviewed before May 2023. There were 2 interview rounds.
(2 Questions)
- Q1. Gradient descent
- Q2. Precision Recall
(2 Questions)
- Q1. Where you see yourslef in next 5 year
- Q2. Difficult situation which you face in ur last job
Intellect Design Arena interview questions for designations
Interview questions from similar companies
Selected
I applied via Company Website and was interviewed in Dec 2024. There were 3 interview rounds.
Basic self evaluation test.
(3 Questions)
- Q1. What project I have completed and follow-up questions on that?
- Q2. How to handle class imbalance.
- Ans.
Handling class imbalance involves techniques like resampling, using different algorithms, and adjusting class weights.
Use resampling techniques like oversampling or undersampling to balance the classes.
Utilize algorithms that are robust to class imbalance, such as Random Forest, XGBoost, or SVM.
Adjust class weights in the model to give more importance to minority class.
Use evaluation metrics like F1 score, precision, r...
- Q3. Basic Python coding questions.
(2 Questions)
- Q1. Data-related questions.
- Q2. ML Ops questions.
Interview Preparation Tips
- Python
- MLOPS
Selected
I applied via Approached by Company and was interviewed in Nov 2024. There were 3 interview rounds.
(1 Question)
- Q1. Basicn details to check for qualifications
(1 Question)
- Q1. About my projects
(1 Question)
- Q1. More details about ML models
Selected
I applied via Company Website and was interviewed in Jul 2024. There were 5 interview rounds.
(1 Question)
- Q1. Basic ml related questions,past project discussion
(1 Question)
- Q1. AI, ML related, statistical analysis of the graph.
(1 Question)
- Q1. Work culture, check
(1 Question)
- Q1. Work culture, project discussion, scenario based questions
(1 Question)
- Q1. Basic HR questions, why sap salary discussions
(3 Questions)
- Q1. About projects and then questions related to ML and DL. Mostly focused on DL part
- Q2. What is the difference between Adam optimizer and Gradient Descent Optimizer?
- Ans.
Adam optimizer is an extension to the Gradient Descent optimizer with adaptive learning rates and momentum.
Adam optimizer combines the benefits of both AdaGrad and RMSProp optimizers.
Adam optimizer uses adaptive learning rates for each parameter.
Gradient Descent optimizer has a fixed learning rate for all parameters.
Adam optimizer includes momentum to speed up convergence.
Gradient Descent optimizer updates parameters b...
- Q3. When to use Relu and when not?
- Ans.
Use ReLU for hidden layers in deep neural networks, avoid for output layers.
ReLU is commonly used in hidden layers to introduce non-linearity and speed up convergence.
Avoid using ReLU in output layers for regression tasks as it can lead to vanishing gradients.
Consider using Leaky ReLU or Sigmoid for output layers depending on the task.
ReLU is computationally efficient and helps in preventing the vanishing gradient prob...
Skills evaluated in this interview
(2 Questions)
- Q1. Given a situation how do you handle different cases
- Q2. Given a proab stats question
Interview Preparation Tips
(2 Questions)
- Q1. Basic pandas questions on dataframes
- Q2. Some quiz questions
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Intellect Design Arena Data Scientist Interview Process
based on 4 interviews
1 Interview rounds
- Technical Round
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