Quantiphi Analytics Solutions Private Limited Interview Questions, Process, and Tips

Q1. About machine learning basics and on resume
Add your answer

Q1. About resume and deep learning a bit off
Add your answer

Q1. I didn't make up to HR round
Add your answer

Q1. Azure Databricks Configuration related question
Add your answer

Q1. Azure Databricks
Add your answer
Q2. Python Coding Q&As
Add your answer

Two questions were there

General Aptitude test

Topic was privatisation of Indian Railways

Q1. How will u count the Number of white vehicles from Patna to kanpur
Ans. 
Counting white vehicles from Patna to Kanpur involves visually identifying and tallying them along the route.
• Observe vehicles passing by on the route

• Note down the count of white vehicles

• Repeat the process until reaching Kanpur

• Use technology like cameras or drones for accurate counting if needed
Answered by AI
Add your answer
Q2. Python Concepts
Add your answer

4 sections Quantitative Analysis, Programming Based (output analysis, error detection), Aptitude and ML Engineering ( activation functions, models etc basics)

Q1. Merge Sort simple divide and conquer but a pain to execute they really just look for how logical you are and if your approach is correct
Add your answer
Q2. Talk about your projects previous experience etc
Add your answer

Q1. What do you think about Q
Add your answer
Q2. Personality based questions like what are your hobbies, what drives you etc
Add your answer

There were easy to medium dsa questions

Q1. Oops concept, explain each of them
Ans. 
Object-oriented programming concepts that help in organizing and structuring code.
• Encapsulation: Bundling data and methods that operate on the data into a single unit (class).

• Inheritance: Allowing a class to inherit properties and behavior from another class.

• Polymorphism: Ability to present the same interface for different data types.

• Abstraction: Hiding the complex implementation details and showing only the necessary
Answered by AI
Add your answer
Q2. Multithreading questions
Add your answer

Q1. In depth questions on every ml algorithm from supervised to un supervised algorithms
Add your answer
Q2. How to over come over fitting
Ans. 
To overcome overfitting, use techniques like cross-validation, regularization, early stopping, and increasing training data.
• Use cross-validation to evaluate model performance on different subsets of data.

• Apply regularization techniques like L1 or L2 regularization to penalize large coefficients.

• Implement early stopping to stop training when validation error starts to increase.

• Increase training data to provide more dive
Answered by AI
Add your answer
Q3. What is PCA, how to do feature selection
Ans. 
PCA is a dimensionality reduction technique used to reduce the number of features in a dataset while preserving the most important information.
• PCA stands for Principal Component Analysis

• It works by finding the directions (principal components) in which the data varies the most

• These principal components are orthogonal to each other and capture the maximum variance in the data

• Feature selection can be done by selecting th...read more
Answered by AI
Add your answer
Q4. Project based questions
Add your answer

Q1. Scenario based questions on implementing NLP use case
Add your answer