Python Programming

SECTION 1 – Python Programming Fundamentals (Q1–Q20)

Q1. What is Python and why is it widely used in Data Science?

Explanation:

Python is a high-level, interpreted programming language known for its clean syntax and readability.
It allows developers to write fewer lines of code compared to other languages.
Python has powerful libraries such as NumPy, Pandas, Matplotlib, and Scikit-learn that simplify data analysis and machine learning.
Its large community provides continuous support and open-source tools.
Because of this ecosystem, Python is the first choice for data scientists and analysts.

Example:

print("Welcome to Data Science")

Tip:

Say: Python combines simplicity with a powerful ecosystem.

Q2. What are variables in Python?

Explanation:

Variables store values in memory so they can be reused later.
Python uses dynamic typing, meaning you don’t need to declare variable types.
A variable can change its value during program execution.
Variables make programs readable and organized.
They are essential for storing intermediate results.

Example:

age = 25
name = "Sumit"
print(age, name)

Tip:

Mention: Python variables are dynamically typed.

Q3. Explain Python data types.

Explanation:

Data types define the type of value stored in variables.
Common built-in types include int, float, string, list, tuple, and dictionary.
Python automatically assigns the type at runtime.
Each data type supports different operations.
Understanding data types prevents runtime errors.

Example:

x = 10        # int
y = 3.5       # float
z = "Hello"   # string

Tip:

Always highlight dynamic typing in interviews.

Q4. What is a list in Python?

Explanation:

A list is an ordered and mutable collection of elements.
It allows duplicate values.
Items can be accessed using index positions.
Lists are commonly used for storing multiple values.
They are heavily used in data processing pipelines.

Example:

numbers = [1, 2, 3, 4]
numbers.append(5)
print(numbers)

Tip:

Say: Lists are changeable collections.

Q5. What is a tuple?

Explanation:

A tuple is similar to a list but immutable.
Once created, its values cannot be changed.
Tuples are faster than lists.
They are used when data should not be modified.
Often used for fixed datasets.

Example:

coords = (10, 20)
print(coords)

Tip:

Tuple = read-only list.

Q6. What is a dictionary?

Explanation:

A dictionary stores data in key-value pairs.
Keys must be unique.
Values can be any data type.
Dictionaries are optimized for fast lookup.
They are widely used for structured data.

Example:

user = {"name": "Sumit", "age": 30}
print(user["name"])

Tip:

Dictionary = labeled data storage.

Q7. What is a function in Python?

Explanation:

A function is a reusable block of code.
It performs a specific task.
Functions improve readability.
They reduce code duplication.
Large applications depend heavily on functions.

Example:

def add(a, b):
    return a + b

print(add(5, 3))

Tip:

Functions promote modular programming.

Q8. What is a lambda function?

Explanation:

Lambda is an anonymous one-line function.
Used for short operations.
Often combined with map or filter.
Improves code compactness.
Avoids writing full function blocks.

Example:

square = lambda x: x * x
print(square(4))

Tip:

Lambda = small temporary function.

Q9. What is conditional statement in Python?

Explanation:

Conditional statements control program flow.
They execute code based on conditions.
Python uses if, elif, else.
Helps make decisions in programs.
Used in validations and logic building.

Example:

age = 18
if age >= 18:
    print("Eligible")
else:
    print("Not Eligible")

Tip:

Conditionals drive business logic.

Q10. What are loops?

Explanation:

Loops repeat code execution.
Python provides for and while loops.
They reduce repetitive code.
Used in data iteration.
Essential for automation.

Example:

for i in range(3):
    print(i)

Tip:

Loops automate repetitive tasks.

Q11. What is exception handling?

Explanation:

Exception handling prevents program crashes.
Uses try, except blocks.
Handles runtime errors gracefully.
Improves application stability.
Important in production systems.

Example:

try:
    print(10/0)
except:
    print("Error occurred")

Tip:

Mention graceful error handling.

Q12. What is list comprehension?

Explanation:

List comprehension creates lists in one line.
Combines loop and condition.
Improves performance.
Makes code concise.
Widely used in data transformations.

Example:

squares = [x*x for x in range(5)]
print(squares)

Tip:

Compact alternative to loops.

Q13. What is pip?

Explanation:

pip is Python package manager.
Used to install external libraries.
Downloads packages from PyPI.
Essential for project setup.
Common in professional environments.

Example:

pip install pandas

Tip:

pip manages dependencies.

Q14. What is a virtual environment?

Explanation:

Virtual environment isolates project libraries.
Prevents version conflicts.
Each project has separate packages.
Improves reproducibility.
Critical in enterprise projects.

Example:

python -m venv env

Tip:

Always use venv in real projects.

Q15. What is JSON?

Explanation:

JSON is a data exchange format.
Used between client and server.
Easy to read and write.
Common in APIs.
Python supports JSON natively.

Example:

import json
data = {"name":"Sumit"}
print(json.dumps(data))

Tip:

JSON is standard API format.

Q16. What are modules?

Explanation:

Modules are Python files containing code.
Allow code reuse.
Imported using import keyword.
Improve organization.
Used in large applications.

Example:

import math
print(math.sqrt(16))

Tip:

Modules help scale projects.

Q17. What is map()?

Explanation:

map applies function to each item.
Returns iterable.
Avoids manual loops.
Used for transformations.
Often combined with lambda.

Example:

nums = [1,2,3]
print(list(map(lambda x:x*2, nums)))

Tip:

map = bulk operation.

Q18. What is filter()?

Explanation:

filter selects elements based on condition.
Returns filtered values.
Reduces manual checks.
Used in cleaning data.
Works with lambda.

Example:

nums=[1,2,3,4]
print(list(filter(lambda x:x>2, nums)))

Tip:

filter removes unwanted data.

Q19. What is type casting?

Explanation:

Type casting converts one data type to another.
Used when input is string.
Avoids type errors.
Common in user input handling.
Python provides int(), float(), str().

Example:

x="10"
print(int(x))

Tip:

Casting prevents runtime issues.

Q20. What is Python interpreter?

Explanation:

Interpreter executes Python code line by line.
No compilation needed.
Helps in debugging.
Provides instant feedback.
Makes Python interactive.

Example:

python file.py

Tip:

Python is interpreted language.


SECTION 2 — OBJECT-ORIENTED PROGRAMMING (OOP)

Q21. What is Object-Oriented Programming (OOP)?

Explanation:

Object-Oriented Programming is a way of organizing code using objects and classes.
It helps structure large applications into reusable components.
OOP improves readability and maintainability.
Python supports full OOP features.
It is widely used in enterprise-level systems.

Example:

class User:
    pass

Tip:

Say: OOP models real-world entities using code.

Q22. What is a class?

Explanation:

A class is a blueprint for creating objects.
It defines properties and behaviors.
Multiple objects can be created from one class.
Classes help organize related data and functions.
They form the base of OOP.

Example:

class Car:
    pass

Tip:

Class = design, Object = real instance.

Q23. What is an object?

Explanation:

An object is an instance of a class.
It represents real data created from a class.
Objects contain actual values.
They allow access to class methods.
Each object has its own memory.

Example:

car1 = Car()

Tip:

Objects bring classes to life.

Q24. What is __init__ method?

Explanation:

__init__ is a constructor.
It runs automatically when object is created.
Used to initialize values.
Helps assign default data.
Every class can have its own constructor.

Example:

class User:
    def __init__(self, name):
        self.name = name

Tip:

Say: Constructor initializes object state.

Q25. What is self?

Explanation:

self represents current object.
It allows access to class variables.
Must be first parameter in methods.
Connects object with class.
Without self, variables become local.

Example:

def show(self):
    print(self.name)

Tip:

Self = current instance.

Q26. What is encapsulation?

Explanation:

Encapsulation hides internal data.
It protects variables from outside access.
Implemented using private variables.
Improves security.
Makes code modular.

Example:

class A:
    __x = 10

Tip:

Encapsulation = data protection.

Q27. What is inheritance?

Explanation:

Inheritance allows one class to use another class.
Child class inherits parent properties.
Reduces duplicate code.
Improves reusability.
Used heavily in frameworks.

Example:

class B(A):
    pass

Tip:

Say: Reuse existing logic.

Q28. What is polymorphism?

Explanation:

Polymorphism means many forms.
Same method works differently.
Allows flexible design.
Used with inheritance.
Improves scalability.

Example:

print(len("Hi"))
print(len([1,2,3]))

Tip:

Same function, different behavior.

Q29. What is abstraction?

Explanation:

Abstraction hides complex logic.
Shows only necessary details.
Implemented using abstract classes.
Improves clarity.
Used in large systems.

Example:

from abc import ABC

Tip:

Abstraction focuses on what, not how.

Q30. What is method overriding?

Explanation:

Child class changes parent method.
Provides custom behavior.
Used with inheritance.
Improves flexibility.
Common in APIs.

Example:

class Child(Parent):
    def show(self):
        print("Child")

Tip:

Overriding = redefining method.

Q31. What is method overloading?

Explanation:

Multiple methods with same name.
Python handles using default arguments.
Improves readability.
Not native like Java.
Simulated in Python.

Example:

def add(a,b=0):
    return a+b

Tip:

Python uses default parameters.

Q32. What are class variables?

Explanation:

Shared among all objects.
Defined outside constructor.
Single copy in memory.
Used for common data.
Accessed using class name.

Example:

class A:
    x = 10

Tip:

Class variables are global inside class.

Q33. What are instance variables?

Explanation:

Belong to specific object.
Defined inside constructor.
Each object gets separate copy.
Used for personal data.
Stored using self.

Example:

self.name = name

Tip:

Instance variables are object-specific.

Q34. What is super()?

Explanation:

Calls parent class methods.
Used inside child class.
Avoids duplication.
Improves inheritance flow.
Common in constructors.

Example:

super().__init__()

Tip:

super() accesses parent.

Q35. What is multiple inheritance?

Explanation:

Child inherits from multiple parents.
Python supports this feature.
Improves flexibility.
Can create complexity.
Follow MRO.

Example:

class C(A,B):
    pass

Tip:

Mention MRO in interviews.

Q36. What is MRO?

Explanation:

Method Resolution Order.
Defines method search sequence.
Important in multiple inheritance.
Python follows C3 algorithm.
Avoids ambiguity.

Example:

ClassName.mro()

Tip:

Say: Python uses C3 linearization.

Q37. What are magic methods?

Explanation:

Special methods with double underscore.
Control object behavior.
Examples: init, str.
Used internally by Python.
Improve customization.

Example:

def __str__(self):
    return "User"

Tip:

Magic methods customize objects.

Q38. What is composition?

Explanation:

Class contains another class.
Alternative to inheritance.
Better loose coupling.
Improves modularity.
Used in clean architecture.

Example:

class Engine:
    pass

Tip:

Prefer composition over inheritance.

Q39. What is object cloning?

Explanation:

Creating copy of object.
Uses copy module.
Shallow or deep copy.
Used in data duplication.
Prevents side effects.

Example:

import copy

Tip:

Know shallow vs deep copy.

Q40. What is garbage collection?

Explanation:

Python automatically deletes unused objects.
Frees memory.
Handled by reference counting.
Improves performance.
No manual delete needed.

Example:

import gc

Tip:

Say: Python has automatic memory management.

SECTION 3 – Core Python + Data Structures (Q41–Q60)


Q41. What is a stack data structure and how can you implement it in Python?

Explanation:

A stack follows the LIFO (Last In First Out) principle.
The last element added is the first one removed.
Stacks are used in undo operations, browser history, and expression evaluation.
Python does not have a built-in stack class, but lists are commonly used.
Understanding stacks is important for algorithm interviews.

Example:

stack = []

# push
stack.append(10)
stack.append(20)

# pop
print(stack.pop())

print(stack)

Tip:

Say: Stack uses LIFO and Python list supports it using append() and pop().

Q42. What is a queue and how is it different from a stack?

Explanation:

A queue follows FIFO (First In First Out).
The first inserted element is removed first.
Queues are used in task scheduling and message processing.
Python provides deque from collections for efficient queues.
Unlike stack, removal happens from the front.

Example:

from collections import deque

queue = deque()
queue.append(1)
queue.append(2)

print(queue.popleft())

Tip:

Queue = FIFO, Stack = LIFO.

Q43. What is slicing in Python?

Explanation:

Slicing extracts part of a sequence.
Works on lists, strings, and tuples.
Uses start, stop, and step.
Very useful in data preprocessing.
Helps avoid loops.

Example:

nums = [1,2,3,4,5]
print(nums[1:4])

Tip:

Syntax: [start:end:step]

Q44. What is deep copy vs shallow copy?

Explanation:

Shallow copy copies references.
Deep copy copies actual objects.
Changes in shallow copy affect original.
Deep copy is fully independent.
Important when handling nested data.

Example:

import copy

a = [[1,2]]
b = copy.deepcopy(a)

Tip:

Mention copy module in interviews.

Q45. What is enumerate()?

Explanation:

enumerate() returns index and value together.
Avoids manual counter variables.
Improves readability.
Often used in loops.
Very helpful in data labeling.

Example:

names = ["A","B","C"]
for i,v in enumerate(names):
    print(i,v)

Tip:

enumerate = index + value.

Q46. What is zip()?

Explanation:

zip() combines multiple iterables.
Pairs elements by index.
Used in feature engineering.
Stops at shortest iterable.
Common in parallel looping.

Example:

a = [1,2]
b = ["x","y"]

print(list(zip(a,b)))

Tip:

zip merges collections.

Q47. What are sets?

Explanation:

Sets store unique elements.
They are unordered.
Used to remove duplicates.
Support mathematical operations.
Very fast lookup.

Example:

nums = {1,2,2,3}
print(nums)

Tip:

Set removes duplicates automatically.

Q48. What is recursion?

Explanation:

Recursion is function calling itself.
Used for tree and graph problems.
Must have base condition.
Without base case causes infinite loop.
Used in factorial and traversal.

Example:

def fact(n):
    if n==1:
        return 1
    return n*fact(n-1)

Tip:

Always explain base case.

Q49. What is generator?

Explanation:

Generators produce values one at a time.
Use yield instead of return.
Save memory.
Used in large data pipelines.
Lazy evaluation.

Example:

def gen():
    yield 1
    yield 2

for x in gen():
    print(x)

Tip:

Generators are memory efficient.

Q50. What is iterator?

Explanation:

Iterator allows traversing elements.
Uses iter and next.
Generators are iterators.
Used internally by loops.
Important for custom data structures.

Example:

nums = iter([1,2,3])
print(next(nums))

Tip:

Iterator = object with next().

Q51. What is global vs local variable?

Explanation:

Local variables exist inside function.
Global variables accessible everywhere.
Use global keyword to modify.
Local scope preferred.
Global variables can cause bugs.

Example:

x=10
def show():
    global x
    x=20

Tip:

Avoid globals in production.

Q52. What is args and kwargs?

Explanation:

*args handles multiple positional arguments.
**kwargs handles keyword arguments.
Makes functions flexible.
Used in frameworks.
Helps build reusable APIs.

Example:

def demo(*args, **kwargs):
    print(args, kwargs)

Tip:

Mention dynamic arguments.

Q53. What is slicing assignment?

Explanation:

Allows replacing list portions.
Powerful list modification.
Supports bulk updates.
Used in data cleaning.
Avoids loops.

Example:

a=[1,2,3,4]
a[1:3]=[9,9]

Tip:

Advanced list manipulation.

Q54. What is mutable vs immutable?

Explanation:

Mutable objects can change.
Immutable cannot.
Lists mutable. Tuples immutable.
Important for memory behavior.
Affects function arguments.

Example:

a=[1,2]
b=(1,2)

Tip:

Explain with list vs tuple.

Q55. What is Python memory management?

Explanation:

Python uses automatic garbage collection.
Reference counting used.
Unused objects deleted.
Developer does not manage memory.
Improves productivity.

Example:

import gc

Tip:

Python handles memory automatically.

Q56. What is decorator?

Explanation:

Decorators modify functions.
Use @ symbol.
Add behavior without changing code.
Used in logging/authentication.
Advanced interview topic.

Example:

def mydec(func):
    def wrap():
        print("Before")
        func()
    return wrap

Tip:

Decorator wraps function.

Q57. What is context manager?

Explanation:

Manages resources.
Uses with keyword.
Automatically closes files.
Improves safety.
Used in file handling.

Example:

with open("a.txt") as f:
    print(f.read())

Tip:

with handles cleanup.

Q58. What is threading?

Explanation:

Threading allows parallel tasks.
Improves performance for IO.
Python has GIL.
Used in networking.
Handled via threading module.

Example:

import threading

Tip:

Mention GIL.

Q59. What is multiprocessing?

Explanation:

Uses multiple CPU cores.
Bypasses GIL.
Used in heavy computation.
Faster than threading for CPU tasks.
Important for ML pipelines.

Example:

import multiprocessing

Tip:

Multiprocessing for CPU tasks.

Q60. What is Python package?

Explanation:

Package is collection of modules.
Has init.py.
Organizes code.
Used in real projects.
Helps scaling applications.

Example:

import mypackage

Tip:

Package = folder of modules.

SECTION 4 – Advanced Python (Q61–Q80)

Real-world interview style • Big MNC / Big-4 level • Beginner → Advanced
Format: Q → Explanation (5+ lines) → Example → Tip

Q61. What is the Global Interpreter Lock (GIL) and why does it matter?

Explanation:

The Global Interpreter Lock (GIL) is a mutex in CPython that allows only one thread to execute Python bytecode at a time.
It exists to simplify memory management and make Python thread-safe.
Because of the GIL, CPU-bound multithreaded programs do not scale across cores.
However, I/O-bound programs still benefit from threading.
For CPU-heavy tasks, multiprocessing is preferred.

Example:

import threading

def task():
    for i in range(1000000):
        pass

t1 = threading.Thread(target=task)
t2 = threading.Thread(target=task)

t1.start()
t2.start()

Tip:

In interviews say: Threading for I/O, multiprocessing for CPU.

Q62. What is a lambda function and where is it used?

Explanation:

A lambda function is an anonymous one-line function.
It is commonly used with map(), filter(), and sorted().
Lambda improves readability for small operations.
It cannot contain multiple statements.
Frequently used in data science pipelines.

Example:

square = lambda x: x*x
print(square(5))

Tip:

Lambda = short temporary function.

Q63. Explain map(), filter(), and reduce().

Explanation:

map() applies a function to each element.
filter() selects elements based on condition.
reduce() aggregates values into one result.
They support functional programming.
Used heavily in data transformations.

Example:

from functools import reduce

nums = [1,2,3,4]

print(list(map(lambda x:x*2, nums)))
print(list(filter(lambda x:x>2, nums)))
print(reduce(lambda a,b:a+b, nums))

Tip:

Mention functional programming concept.

Q64. What is pickle in Python?

Explanation:

Pickle serializes Python objects to binary format.
Used to save ML models.
Not secure for untrusted sources.
Allows object persistence.
Common in deployment pipelines.

Example:

import pickle

data = {"a":1}
pickle.dump(data, open("file.pkl","wb"))

Tip:

Never load pickle from unknown sources.

Q65. What is virtual environment?

Explanation:

Virtual environments isolate project dependencies.
Prevent version conflicts.
Each project gets its own packages.
Essential in production systems.
Created using venv or virtualenv.

Example:

python -m venv myenv

Tip:

Always use venv in real projects.

Q66. What is name == "main"?

Explanation:

This checks whether file is run directly.
Prevents code from executing during imports.
Improves modularity.
Used in scripts and libraries.
Common interview question.

Example:

if __name__ == "__main__":
    print("Running directly")

Tip:

Explain module execution.

Q67. What is monkey patching?

Explanation:

Monkey patching modifies classes or functions at runtime.
Used in testing.
Dangerous in production.
Can cause unexpected behavior.
Advanced Python concept.

Example:

class A:
    def show(self):
        print("Old")

A.show = lambda self: print("New")

Tip:

Say it carefully—rare in production.

Q68. What is serialization vs deserialization?

Explanation:

Serialization converts object to storage format.
Deserialization restores it back.
Used in APIs and ML.
JSON and pickle common.
Important in distributed systems.

Example:

import json

x = json.dumps({"a":1})
print(json.loads(x))

Tip:

JSON for APIs, Pickle for Python objects.

Q69. What is asyncio?

Explanation:

asyncio enables asynchronous programming.
Used for concurrent I/O operations.
Uses async/await syntax.
Improves performance for network apps.
Common in microservices.

Example:

import asyncio

async def main():
    print("Hello")

asyncio.run(main())

Tip:

Async is for I/O concurrency.

Q70. What is metaclass?

Explanation:

Metaclass defines class behavior.
Class of a class.
Rarely used directly.
Used in frameworks like Django.
Advanced Python topic.

Example:

class Meta(type):
    pass

class A(metaclass=Meta):
    pass

Tip:

Say: metaclass creates classes.

Q71. What is slot in Python?

Explanation:

slots restricts attributes.
Reduces memory usage.
Improves performance.
Used in large systems.
Prevents dynamic attributes.

Example:

class A:
    __slots__ = ['x']

Tip:

Slots optimize memory.

Q72. What is heapq?

Explanation:

heapq implements priority queue.
Always pops smallest element.
Used in scheduling.
Fast O(log n).
Common algorithm question.

Example:

import heapq

h=[3,1,2]
heapq.heapify(h)
print(heapq.heappop(h))

Tip:

Heap = priority queue.

Q73. What is bisect?

Explanation:

bisect maintains sorted order.
Used in binary search.
Very fast insertion.
Useful in ranking systems.
Avoids manual searching.

Example:

import bisect

a=[1,3,5]
bisect.insort(a,4)

Tip:

bisect = binary insert.

Q74. What is traceback?

Explanation:

Traceback shows error path.
Helps debugging.
Displays call stack.
Used in logging.
Critical for production errors.

Example:

import traceback

Tip:

Traceback explains crash.

Q75. What is profiling?

Explanation:

Profiling finds slow code.
Measures execution time.
Used for optimization.
cProfile is common.
Important for performance tuning.

Example:

import cProfile

Tip:

Profiling before optimizing.

Q76. What is memory leak?

Explanation:

Memory leak means unused memory not released.
Occurs with circular references.
Leads to crashes.
gc module helps.
Critical in long-running apps.

Example:

import gc

Tip:

Mention garbage collector.

Q77. What is unit testing?

Explanation:

Unit testing validates individual components.
Ensures code quality.
Uses unittest or pytest.
Mandatory in MNCs.
Part of CI/CD.

Example:

import unittest

Tip:

Testing improves reliability.

Q78. What is mocking?

Explanation:

Mocking simulates real objects.
Used in testing.
Avoids external dependencies.
Speeds tests.
Uses unittest.mock.

Example:

from unittest import mock

Tip:

Mock replaces real services.

Q79. What is CI/CD in Python projects?

Explanation:

CI = continuous integration.
CD = continuous deployment.
Automates testing and release.
Used with GitHub Actions/Jenkins.
Standard in enterprises.

Example:

# pipeline runs tests automatically

Tip:

Mention automation.

Q80. How is Python used in Data Science pipelines?

Explanation:

Python handles data ingestion.
Performs cleaning and modeling.
Supports visualization.
Deploys models via APIs.
Dominant DS language.

Example:

import pandas as pd
import numpy as np

Tip:

Explain full pipeline flow.

SECTION 5 – Enterprise Python + System Design + Real Interview Scenarios

(Q81–Q100)


Q81. How do you design a scalable Python backend system?

Explanation:

A scalable backend separates concerns into services.
API layer handles requests.
Business logic layer processes data.
Database layer stores information.
Caching (Redis), message queues, and load balancers improve performance.
Horizontal scaling is preferred over vertical.

Example:

# Flask API layer
from flask import Flask
app = Flask(__name__)

@app.route("/health")
def health():
    return "OK"

Tip:

Mention: API + Service + DB + Cache + Queue.

Q82. How do you optimize Python performance in production?

Explanation:

Profile code to find bottlenecks.
Use efficient algorithms and data structures.
Avoid unnecessary loops.
Use multiprocessing for CPU tasks.
Use async for I/O tasks.
Cache expensive operations.

Example:

from functools import lru_cache

@lru_cache()
def heavy():
    return sum(range(1000000))

Tip:

Always profile first.

Q83. How do you handle large datasets in Python?

Explanation:

Never load everything into memory.
Use chunk processing.
Use generators.
Prefer pandas chunksize.
Use Dask or Spark for big data.
Stream data when possible.

Example:

import pandas as pd

for chunk in pd.read_csv("data.csv", chunksize=1000):
    print(chunk.head())

Tip:

Say: streaming over loading.

Q84. Explain REST API development in Python.

Explanation:

REST uses HTTP verbs (GET, POST, PUT, DELETE).
Python frameworks like Flask/FastAPI handle APIs.
JSON is standard format.
Authentication via JWT or OAuth.
Used for ML deployment.

Example:

from flask import Flask, jsonify
app = Flask(__name__)

@app.route("/users")
def users():
    return jsonify({"name":"Sumit"})

Tip:

Mention stateless architecture.

Q85. How do you deploy Python applications?

Explanation:

Code is containerized using Docker.
Deployed to cloud servers.
CI/CD pipelines automate releases.
Gunicorn + Nginx handle traffic.
Monitoring tools track health.

Example:

gunicorn app:app

Tip:

Say Docker + CI/CD.

Q86. What is microservice architecture?

Explanation:

Application split into small services.
Each service handles one responsibility.
Communicates via APIs.
Improves scalability.
Allows independent deployment.

Example:

# User service
@app.route("/user")
def user():
    return "User Service"

Tip:

Explain service isolation.

Q87. How do you secure Python applications?

Explanation:

Use HTTPS.
Validate inputs.
Hash passwords.
Use environment variables.
Implement authentication.

Example:

import hashlib

pwd = hashlib.sha256("pass".encode()).hexdigest()

Tip:

Never store plain passwords.

Q88. How do you log errors?

Explanation:

Logging tracks runtime behavior.
Helps debugging.
Used with monitoring tools.
Logs saved centrally.
Essential for production.

Example:

import logging
logging.error("Something failed")

Tip:

Logs are production eyes.

Q89. What is message queue?

Explanation:

Queues handle background jobs.
Prevents blocking main app.
Used for emails, ML tasks.
RabbitMQ/Kafka common.
Improves system resilience.

Example:

# producer sends message to queue

Tip:

Queues = async processing.

Q90. Explain caching strategy.

Explanation:

Cache stores frequent data.
Reduces DB load.
Improves response time.
Redis commonly used.
Critical for scalability.

Example:

# Redis example placeholder

Tip:

Cache before database.

Q91. How do you handle concurrency?

Explanation:

Threads for I/O.
Processes for CPU.
Async for network.
Avoid race conditions.
Use locks if needed.

Example:

import multiprocessing

Tip:

Pick correct model.

Q92. What is containerization?

Explanation:

Containers package app + dependencies.
Ensures same behavior everywhere.
Docker used widely.
Simplifies deployment.
Standard in enterprises.

Example:

FROM python:3.10

Tip:

Docker is industry default.

Q93. Explain model deployment lifecycle.

Explanation:

Train model.
Validate.
Serialize.
Expose API.
Monitor drift.
Retrain periodically.

Example:

import pickle

Tip:

Mention monitoring.

Q94. What is data drift?

Explanation:

Input data changes over time.
Model accuracy drops.
Detected via statistics.
Requires retraining.
Major ML challenge.

Example:

# compare distributions

Tip:

Always monitor drift.

Q95. Explain ETL pipelines.

Explanation:

Extract data.
Transform format.
Load to warehouse.
Python automates pipelines.
Core data engineering task.

Example:

# extract → transform → load

Tip:

ETL = backbone of DS.

Q96. How do you write production-ready Python?

Explanation:

Follow PEP8.
Add tests.
Handle exceptions.
Log everything.
Document code.

Example:

def add(a,b):
    return a+b

Tip:

Readable code wins interviews.

Q97. How do you debug Python?

Explanation:

Use print.
Use pdb.
Check logs.
Reproduce bugs.
Write tests.

Example:

import pdb; pdb.set_trace()

Tip:

Explain systematic debugging.

Q98. Explain system design interview approach.

Explanation:

Clarify requirements.
Estimate scale.
Design components.
Identify bottlenecks.
Optimize.

Example:

# architecture diagram mentally

Tip:

Think aloud.

Q99. How do you stay production safe?

Explanation:

Use feature flags.
Gradual rollout.
Rollback strategy.
Health checks.
Monitoring.

Example:

# health endpoint

Tip:

Safety first.

Q100. Why should companies hire you as Python developer?

Explanation:

Strong fundamentals.
Clean coding habits.
Problem-solving mindset.
System design understanding.
Production experience.

Example:

print("I build reliable systems")

Tip:

Highlight impact + learning attitude.

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