- Tell Me About Yourself
Prepare a 60–90 second introduction:
- Education
- Skills
- Projects
- Experience
- Target role
- Python Basics
Prepare:
- List vs Tuple vs Dictionary
- Mutable vs Immutable
- "==" vs "is"
- "None" vs "False"
- "*args", "**kwargs"
- List comprehension
- Lambda
- Exception handling
Output questions: Be ready to trace small Python code snippets.
- Dictionary
Know valid dictionary keys:
"int", "string", "tuple"
"list", "dict", "set"
Reason: Keys must be hashable.
- OOP
Four concepts:
- Encapsulation
- Inheritance
- Polymorphism
- Abstraction
Also prepare: class/object, constructor, "self", overriding, overloading.
Important: Python doesn't support traditional method overloading like Java/C++.
- Resume-Based Questions
Everything on your resume can be questioned.
Postman: What is it? Why use it? API testing?
Full Stack: What is frontend/backend? REST API? Database? How do they communicate?
Projects: Why this technology? Your contribution? Challenges?
- ML Basics
Prepare:
- Supervised vs Unsupervised
- Classification vs Regression
- Overfitting/Underfitting
- Feature Engineering
- Missing Values
- Accuracy, Precision, Recall, F1
- Confusion Matrix
- Imbalanced Data
- Logical Reasoning
Example:
11 players, jersey numbers 1–11. 10 players are out. Which remains?
Don't assume—identify which player was not eliminated.
- Real ML Scenario
Accuracy = 88%. Add a new column → 95%. Accept?
No, not immediately.
Check:
- Data leakage
- Preprocessing
- Test-set contamination
- Performance on unseen data
- Other metrics
Remember: Higher accuracy ≠ automatically better model.