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BIOCUBE MATRICS Data Scientist Interview Questions, Process, and Tips

Updated 21 May 2024

BIOCUBE MATRICS Data Scientist Interview Experiences

1 interview found

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

I applied via LinkedIn and was interviewed in Apr 2024. There was 1 interview round.

Round 1 - Technical 

(5 Questions)

  • Q1. What is Dropout & Batch Normalization?
  • Ans. 

    Dropout is a regularization technique to prevent overfitting by randomly setting some neuron outputs to zero during training. Batch Normalization is a technique to normalize the inputs of each layer to improve training speed and stability.

    • Dropout randomly sets a fraction of neuron outputs to zero during training to prevent overfitting.

    • Batch Normalization normalizes the inputs of each layer to improve training speed and...

  • Answered by AI
  • Q2. Explain YOLO architecture, difference with SSD?
  • Ans. 

    YOLO (You Only Look Once) is a real-time object detection system that processes images in a single pass, while SSD (Single Shot MultiBox Detector) is another object detection model that also aims for real-time processing but uses a different approach.

    • YOLO processes images in a single pass, making it faster than SSD which requires multiple passes.

    • SSD uses a fixed grid of boxes at different aspect ratios and scales to de...

  • Answered by AI
  • Q3. What is confusion Matrix?
  • Ans. 

    Confusion Matrix is a table that is often used to describe the performance of a classification model.

    • It is a 2x2 matrix that summarizes the predictions of a classification model.

    • It shows the number of true positives, true negatives, false positives, and false negatives.

    • It is useful for evaluating the performance of a model by calculating metrics like accuracy, precision, recall, and F1 score.

  • Answered by AI
  • Q4. What are optimizers in Deep Learning Models?
  • Ans. 

    Optimizers in Deep Learning Models are algorithms used to minimize the loss function by adjusting the weights of the neural network.

    • Optimizers help in updating the weights of the neural network during training to minimize the loss function.

    • Popular optimizers include Adam, SGD, RMSprop, and Adagrad.

    • Each optimizer has its own way of updating the weights based on gradients and learning rate.

    • Choosing the right optimizer ca...

  • Answered by AI
  • Q5. Difference between Right and Inner Join?
  • Ans. 

    Right join includes all records from the right table and matching records from the left table, while inner join includes only matching records from both tables.

    • Right join keeps all records from the right table, even if there are no matches in the left table.

    • Inner join only includes records that have matching values in both tables.

    • Example: If we have a table of employees and a table of departments, a right join would in...

  • Answered by AI

Interview Preparation Tips

Topics to prepare for BIOCUBE MATRICS Data Scientist interview:
  • Computer Vision
  • Deep Learning
  • Python
  • Machine Learning
  • API
Interview preparation tips for other job seekers - Prepare your resume and Questions about Computer vision & Deep Learning.

Skills evaluated in this interview

Interview questions from similar companies

I applied via Recruitment Consultant and was interviewed in Sep 2020. There were 3 interview rounds.

Interview Questionnaire 

1 Question

  • Q1. Machine learning concepts[Regression and classification] ,Python and Sql Basics

Interview Preparation Tips

Interview preparation tips for other job seekers - Cool

Interview Questionnaire 

1 Question

  • Q1. Please tell me something about yourself.What is your experience? What are your goals and ambitions?Why We should hire you? Strengths and weaknesses etc.

I applied via Campus Placement and was interviewed before Sep 2020. There were 3 interview rounds.

Interview Questionnaire 

1 Question

  • Q1. Nothing much technical

Interview Preparation Tips

Interview preparation tips for other job seekers - 1. Go in formals
2. Fluency in English is important (depends on interview panel)
3. Clarity on what your talking about

I applied via Approached by Company and was interviewed before Sep 2021. There were 3 interview rounds.

Round 1 - Resume Shortlist 
Pro Tip by AmbitionBox:
Don’t add your photo or details such as gender, age, and address in your resume. These details do not add any value.
View all tips
Round 2 - Aptitude Test 

Explain dynamic programming with memoization

Round 3 - HR 

(2 Questions)

  • Q1. Where are you from, and why are you joining the company
  • Q2. Why are you joining the company

Interview Preparation Tips

Interview preparation tips for other job seekers - First, they will ask about the breadth of your ML skills and the depth going forward

Interview Questionnaire 

1 Question

  • Q1. Basic ML
Interview experience
3
Average
Difficulty level
Moderate
Process Duration
2-4 weeks
Result
No response

I applied via Campus Placement and was interviewed in Apr 2023. There were 3 interview rounds.

Round 1 - Resume Shortlist 
Pro Tip by AmbitionBox:
Properly align and format text in your resume. A recruiter will have to spend more time reading poorly aligned text, leading to high chances of rejection.
View all tips
Round 2 - Aptitude Test 

Java script, angular js, node questions

Round 3 - Technical 

(2 Questions)

  • Q1. Asked about Company and why do you choose genpact?
  • Q2. Questions on my projects and college experience

Interview Preparation Tips

Topics to prepare for Genpact Data Scientist interview:
  • SQL
  • DBMS
  • Operating Systems
Interview preparation tips for other job seekers - Projects mostly and dbms sql
Interview experience
3
Average
Difficulty level
-
Process Duration
-
Result
Selected Selected
Round 1 - One-on-one 

(2 Questions)

  • Q1. Difference between supervised and unsupervised learning
  • Ans. 

    Supervised learning uses labeled data to train a model, while unsupervised learning uses unlabeled data.

    • Supervised learning requires a target variable for training the model.

    • Examples of supervised learning include classification and regression.

    • Unsupervised learning finds patterns and relationships in data without a target variable.

    • Examples of unsupervised learning include clustering and dimensionality reduction.

  • Answered by AI
  • Q2. What is sigmoid function
  • Ans. 

    Sigmoid function is a mathematical function that maps any real value to a value between 0 and 1.

    • Used in machine learning for binary classification problems to produce probabilities

    • Commonly used in logistic regression

    • Has an S-shaped curve

    • Equation: f(x) = 1 / (1 + e^(-x))

  • Answered by AI

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

Data Scientist Interview Questions & Answers

Cognizant user image HARSH VARDHAN Sharma

posted on 11 Feb 2023

Interview experience
4
Good
Difficulty level
-
Process Duration
-
Result
-
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. Questions related to machine learning
Round 3 - Technical 

(1 Question)

  • Q1. Question related to machine learning, projects and case study.
Round 4 - HR 

(1 Question)

  • Q1. Salary discussion and position offered.

Interview Preparation Tips

Interview preparation tips for other job seekers - Be thorough with basic data science concepts, projects.

BIOCUBE MATRICS Interview FAQs

How many rounds are there in BIOCUBE MATRICS Data Scientist interview?
BIOCUBE MATRICS interview process usually has 1 rounds. The most common rounds in the BIOCUBE MATRICS interview process are Technical.
How to prepare for BIOCUBE MATRICS 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 BIOCUBE MATRICS. The most common topics and skills that interviewers at BIOCUBE MATRICS expect are Data Science, Machine Learning, Python, Tensorflow and Artificial Intelligence.
What are the top questions asked in BIOCUBE MATRICS Data Scientist interview?

Some of the top questions asked at the BIOCUBE MATRICS Data Scientist interview -

  1. What are optimizers in Deep Learning Mode...read more
  2. What is Dropout & Batch Normalizati...read more
  3. Explain YOLO architecture, difference with S...read more

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BIOCUBE MATRICS Data Scientist Interview Process

based on 1 interview

Interview experience

5
  
Excellent
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BIOCUBE MATRICS Data Scientist Salary
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₹4.8 L/yr - ₹10 L/yr
48% less than the average Data Scientist Salary in India
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