DataToBiz Private Limited Artificial Intelligence Intern Interview Questions and Answers

Q1. When would you choose a linear model over a tree-based model?
Ans. 
Linear models are preferred for simplicity, interpretability, and when data relationships are linear.
• Simplicity: Linear models are easier to implement and understand compared to complex tree-based models.

• Interpretability: Linear models provide clear insights into feature importance and relationships.

• Linearity: When the relationship between features and the target variable is approximately linear, linear models perform ...read more
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Q2. If you had access to user behavior data, how would you use it to improve campaign performance using ML techniques?
Ans. 
Leverage user behavior data to optimize campaigns through targeted strategies and machine learning models.
• Segment users based on behavior patterns to tailor campaigns (e.g., targeting frequent buyers with loyalty rewards).

• Utilize predictive analytics to forecast user engagement and conversion rates, adjusting campaigns accordingly.

• Implement A/B testing to evaluate different campaign strategies and refine them based on ...read more
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Q3. What’s the difference between training a model for content generation vs. classification? Which metrics would you track in each case?
Ans. 
Training models for content generation focuses on creativity, while classification emphasizes accuracy and categorization.
• Objective: Content generation aims to create new text (e.g., writing stories), while classification categorizes existing data (e.g., spam detection).

• Data Type: Content generation uses diverse datasets (e.g., books, articles), whereas classification relies on labeled datasets (e.g., emails labeled as...read more
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Q4. Explain the difference between overfitting and underfitting. How do you prevent each?
Ans. 
Overfitting captures noise in data, while underfitting fails to capture trends. Both hinder model performance.
• Overfitting occurs when a model learns the training data too well, including noise and outliers.

• Example of overfitting: A complex polynomial regression that fits all data points perfectly but performs poorly on new data.

• Underfitting happens when a model is too simple to capture the underlying patterns in the da...read more
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Q1. Resume Discussion
Ans. 
Discussing my resume highlights my skills, experiences, and projects relevant to the AI internship role.
• Education: Pursued a degree in Computer Science with a focus on AI and machine learning.

• Projects: Developed a machine learning model for predicting housing prices using Python and scikit-learn.

• Internship Experience: Completed an internship at XYZ Corp, where I assisted in data analysis and model training.

• Skills: Prof...read more
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Q2. Projects and research paper
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Mcq

Good level question. I have qualified for the next round

Basic to intermediate question, Cleared the test

Q1. Tell me about yourself and introduce yourself
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Q1. Can you tell me about yourself?
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Q2. What are the key features and functionalities of the Django framework?
Ans. 
Django is a high-level Python web framework that promotes rapid development and clean, pragmatic design.
• MTV Architecture: Django follows the Model-Template-View architecture, separating data, presentation, and business logic.

• Admin Interface: Automatically generated admin panel for managing application data, customizable for different user roles.

• ORM: Object-Relational Mapping allows developers to interact with the datab...read more
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Simple coding and scripting round

This round was in front of a panelist you have to code a basic application

Q1. This was system design round where db schema and application outline and logic to build it were asked
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Q1. Here behavioural question were asked by the product owner
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Q1. This is also a coding round where you have to code a small application related to the tech you have exp in
Ans. 
A simple application to manage a list of tasks with CRUD operations.
• Use an array to store tasks, e.g., ['Task 1', 'Task 2'].

• Implement functions for Create, Read, Update, Delete operations.

• Example: function to add a task - function addTask(task) { tasks.push(task); }

• Use a loop to display all tasks - tasks.forEach(task => console.log(task));

• Consider using local storage for persistence in a web application.
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