Compro Technologies Interview Questions and Answers

Q1. Introduce yourself
Ans. 
I am a passionate web developer with experience in front-end and back-end technologies.
• Experienced in HTML, CSS, JavaScript, and various frameworks like React and Angular

• Proficient in server-side languages like Node.js and PHP

• Familiar with databases such as MySQL and MongoDB
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Q1. Roles and responsibilities of my last company
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I was responsible for front-end and back-end development, maintaining and updating websites, and collaborating with designers and stakeholders.
• Developed and maintained websites using HTML, CSS, JavaScript, PHP, and MySQL

• Collaborated with designers to implement visual elements and user experience improvements

• Worked with stakeholders to gather requirements and provide technical solutions

• Ensured websites were responsive a
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Q1. Complete the task with given instruction
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Q1. Technical questions
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Q1. Tell me your family background
Ans. 
I come from a close-knit family with a diverse background and strong values.
• My parents are both educators, instilling in me a love for learning and problem-solving.

• I have two siblings, one pursuing a career in finance and the other in healthcare.

• Family gatherings are always filled with laughter, good food, and lively discussions.
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Q2. Last company's work experience
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I worked as a web developer at a software company, where I was responsible for designing and developing websites and web applications.
• Designed and developed responsive websites using HTML, CSS, and JavaScript

• Collaborated with the design team to create user-friendly interfaces

• Integrated third-party APIs to enhance functionality

• Optimized website performance for speed and SEO

• Provided ongoing maintenance and support for ex
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Q3. How you handle the work pressure
Ans. 
I handle work pressure by prioritizing tasks, setting realistic deadlines, and taking breaks when needed.
• Prioritize tasks based on deadlines and importance

• Break down tasks into smaller, manageable chunks

• Set realistic deadlines and communicate with team members if needed

• Take short breaks to recharge and refocus

• Practice stress-relief techniques such as deep breathing or mindfulness
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Q1. What is difference between random forest and xgboost
Ans. 
Random forest is an ensemble learning method using decision trees, while XGBoost is a gradient boosting algorithm.
• Random forest builds multiple decision trees and combines their predictions, while XGBoost builds trees sequentially to correct errors.

• Random forest is less prone to overfitting compared to XGBoost.

• XGBoost is computationally efficient and often outperforms random forest in terms of predictive accuracy.

• Rando...read more
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Q2. What is your project problem statement
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Q3. How you convince for clients the model is useful, what are the matrix
Ans. 
I convince clients of model usefulness by showcasing its accuracy, precision, recall, and F1 score.
• Explain the model's accuracy in predicting outcomes compared to actual results

• Discuss precision - the proportion of true positive predictions out of all positive predictions

• Highlight recall - the proportion of true positive predictions out of all actual positives

• Mention F1 score - the balance between precision and recall,
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Q1. How you take data from client
Ans. 
I collect data from clients through various methods such as surveys, interviews, API integrations, and data sharing agreements.
• Conduct surveys to gather specific data points from clients

• Interview clients to understand their data needs and preferences

• Integrate with client's systems through APIs to access real-time data

• Establish data sharing agreements to receive data from clients securely
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Q2. What are hyper parameters you turned for your model
Ans. 
Tuned hyperparameters include learning rate, batch size, number of layers, and activation functions.
• Adjusted learning rate to improve model convergence

• Optimized batch size for better training efficiency

• Experimented with different numbers of layers to find optimal model complexity

• Tried various activation functions to enhance model performance
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Q3. What difference model you use in your project
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Q4. How you work with llm
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Q5. What are the features in your data set
Ans. 
The features in the data set include age, gender, income, education level, location, and purchase history.
• Age

• Gender

• Income

• Education level

• Location

• Purchase history
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Q6. What feature engineering you did in project
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Basic aptitude + logical reasoning + technical mcq(Javascript related mcq)

Got rejected in Group discussion.

Q1. Output based Q on javascript.
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Q2. Project related discussion
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2 section
MCQ + CODING(intermediate level)

Q1. Basic questions related to JS, ANGULAR, Node JS, and some Coding questions
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Todo application was the assignment task to complete in one day.

Q1. Personal info about yourself
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Q2. Past working profession and culture and the job requirement standards
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