Top Python Coding & Scripting Interview Questions [2025]

This guide provides 101 scenario-based Python coding and scripting interview questions with detailed answers for developers preparing for technical interviews. Covering coding fundamentals, data structures, algorithms, libraries, error handling, integrations, automation, and real-world applications, it equips candidates to excel in building robust, scalable Python scripts for enterprise environments.

Python Coding Fundamentals

1. What do you do when a Python script fails due to an undefined variable?

An undefined variable error stops script execution, disrupting application flow. Check variable declarations using a debugger like pdb, ensure initialization before use, and implement try-except blocks for error handling. Test fixes in a sandbox environment, commit changes to Git for version control, and use the logging module to track issues. Monitor with Sentry to detect recurring errors, ensuring reliable code execution in production.

2. Why does a Python function return None unexpectedly?

Unexpected None returns occur when a function lacks an explicit return statement, causing logic errors. Validate all code paths, add return statements, and test with pytest to ensure correctness. Log outputs with the logging module for debugging, and monitor with Sentry to prevent None-related issues, ensuring consistent function behavior and reliable script performance in production environments.

3. How do you handle type mismatches in Python script inputs?

Type mismatches cause runtime errors, breaking script functionality. Use isinstance() to verify input types, convert with int() or str(), and wrap logic in try-except blocks. Test input validation in a sandbox environment, log errors with the logging module, and monitor with Sentry to ensure robust input handling and stable script execution in production workflows.

4. When does a Python script fail due to incorrect indentation?

Indentation errors arise from inconsistent spacing or mixing tabs and spaces, halting execution. Python requires consistent indentation per PEP 8. Standardize to four spaces, validate with pylint, and test in a sandbox. Commit to Git, automate linting with pre-commit hooks, and monitor with Sentry to ensure consistent script execution and prevent indentation-related failures in production.

5. Where do you store Python script configuration settings?

Configuration settings ensure script portability and security.

• Use python-dotenv to load .env files for sensitive data.
• Commit non-sensitive configs to Git for version control.
• Automate config loading with scripts for consistency.
• Test configurations in a sandbox environment.
• Monitor with Sentry for access errors.
  This approach ensures secure, reusable configurations across development and production environments.

6. Which Python features improve script readability?

• Type Hints: Clarify variable types for better understanding.
• Docstrings: Document script functionality clearly.
• PEP 8 Compliance: Enforces consistent coding style.
• NamedTuples: Enhance data structure clarity.
• Context Managers: Simplify resource handling with with statements.
  These features improve script maintainability. Test readability in a sandbox and monitor with Sentry for production stability.

7. Who writes reusable Python utility scripts in a team?

Developers create reusable utility scripts, storing them in a shared Git module. They test with pytest to ensure reliability, automate testing with CI/CD pipelines, and integrate logging with the logging module for debugging. Sentry monitoring ensures robust performance, maintaining consistent functionality across team projects in production environments.

8. What causes a Python script to fail with import errors?

Import errors occur from missing modules or incorrect paths, halting script execution. Verify module installation with pip, check sys.path, and test in a sandbox. Update requirements.txt, automate with pipenv, and monitor with Sentry to ensure reliable imports and consistent script execution in production applications.

9. Why does a Python script fail to parse JSON data?

JSON parsing failures result from invalid JSON or encoding issues, disrupting data processing. Validate with json.loads() in a try-except block, test in a sandbox, and log errors with the logging module. Monitor with Sentry to detect issues early, ensuring reliable data handling and script stability in production environments.

10. How do you validate inputs in a Python script?

def validate_input(data, expected_type):
try:
if not isinstance(data, expected_type):
raise ValueError(f"Expected {expected_type}, got {type(data)}")
logging.info("Input validated")
return True
except ValueError as e:
logging.error(f"Validation error: {e}")
return False
Test in a sandbox, automate with pytest, and monitor with Sentry for robust input validation.

11. What do you do when a Python script crashes with a syntax error?

Syntax errors prevent script execution, impacting reliability. Use pylint to identify issues, fix code in a text editor, and test in a sandbox. Commit changes to Git, automate linting with pre-commit hooks, and monitor with Sentry to ensure stable execution and prevent syntax-related failures in production environments.

12. Why does a Python function fail for edge cases?

Edge case failures stem from untested boundary conditions, leading to incorrect outputs. Add unit tests with pytest, validate inputs with assertions, and debug with pdb. Test in a sandbox, log errors with the logging module, and monitor with Sentry to ensure robust function behavior in production scripts.

13. How do you optimize a Python script for memory efficiency?

Memory optimization is critical for efficient scripts. Profile with memory_profiler, use generators for large datasets, and avoid deep copies. Test in a sandbox to verify improvements, log metrics with the logging module, and monitor with Prometheus to ensure efficient memory usage and stable script performance in production environments.

Data Structures and Algorithms

14. What do you do when a list operation raises an IndexError?

An IndexError disrupts list processing, causing script failures. Check indices with len(), use try-except for error handling, and test in a sandbox. Log errors with the logging module, automate tests with pytest, and monitor with Sentry to prevent index-related issues and ensure reliable list operations in production scripts.

15. Why does a dictionary lookup fail with a KeyError?

KeyError occurs when accessing nonexistent keys, breaking script flow. Use dict.get() for safe access, validate keys with the in operator, and test in a sandbox. Log errors with the logging module to track issues, and monitor with Sentry to ensure reliable dictionary operations and prevent lookup failures in production.

16. How do you implement a binary search in a Python script?

def binary_search(arr, target):
left, right = 0, len(arr) - 1
while left <= right:
mid = (left + right) // 2
if arr[mid] == target:
return mid
elif arr[mid] < target:
left = mid + 1
else:
right = mid - 1
return -1
Test with pytest, log results, and monitor with Sentry for reliability.

17. When does a list comprehension cause performance issues?

List comprehensions slow scripts with large datasets due to memory overhead. This impacts performance in production. Use generator expressions for efficiency, profile with cProfile, and test in a sandbox. Automate tests with pytest to validate optimizations. Monitor with Prometheus to ensure efficient execution and prevent slowdowns in production scripts.

18. Where do you store custom data structure code in a script?

Custom data structures enhance script functionality. Store them in a dedicated module in a Git repository for version control.

• Create data_structures.py for organization.
• Commit to GitHub or Bitbucket for traceability.
• Automate tests with pytest for reliability.
• Monitor with Sentry for runtime errors.
• Test in a sandbox for stability.
  This ensures reusable, maintainable code.

19. Which data structure is best for frequent lookups?

Dictionaries offer O(1) average-case lookup speed, ideal for frequent access. Use dict for key-value pairs, test with pytest, and monitor with Sentry for reliability.

• Sets: Fast for membership testing.
• Lists: Avoid due to O(n) lookup time.
• Custom HashMaps: Optimize specific cases.
  Test in a sandbox to confirm performance.

20. Who designs custom algorithms for Python scripts?

Developers design custom algorithms for project-specific needs, implementing them in Git modules. They test with pytest for correctness, optimize with cProfile for performance, and log with the logging module for debugging. Sentry monitoring ensures reliable algorithm execution, maintaining efficiency across team scripts in production environments.

21. What causes a Python set operation to fail unexpectedly?

Set operation failures occur with unhashable types like lists, causing TypeError. Ensure elements are hashable (e.g., tuples), use try-except, and test in a sandbox. Log errors with the logging module, automate tests with pytest, and monitor with Sentry to ensure reliable set operations in production scripts.

22. Why does a sorting algorithm in a script perform poorly?

Poor sorting performance results from inefficient algorithms or large datasets. Use sorted() with Timsort, optimize with key functions, and profile with cProfile. Test in a sandbox, automate with pytest, and monitor with Prometheus to ensure fast, reliable sorting in production scripts without performance degradation.

23. How do you implement a queue in a Python script?

from collections import deque
queue = deque()
def process_task(task):
queue.append(task)
if queue:
current_task = queue.popleft()
logging.info(f"Processing: {current_task}")
return current_task
return None
Test in a sandbox, automate with pytest, and monitor with Sentry for reliable task processing.

24. What do you do when a recursive script causes a stack overflow?

A stack overflow in recursion halts scripts due to excessive call depth. Convert to iterative logic, use tail recursion, or adjust sys.setrecursionlimit(). Test in a sandbox, log with the logging module, and monitor with Sentry to prevent crashes and ensure stable script execution in production.

25. Why does a script fail to process large datasets efficiently?

Large dataset processing fails due to memory-intensive operations. Use generators for incremental processing, optimize with NumPy, and profile with memory_profiler. Test in a sandbox, automate with pytest, and monitor with Prometheus to ensure efficient data handling and prevent performance bottlenecks in production scripts.

26. How do you implement a graph traversal in a Python script?

def bfs(graph, start):
visited = set()
queue = deque([start])
while queue:
node = queue.popleft()
if node not in visited:
visited.add(node)
queue.extend(graph[node])
return visited
Test with pytest, log with the logging module, and monitor with Sentry for reliable traversal.

Libraries and Frameworks

27. What do you do when a Pandas operation fails in a script?

Pandas operation failures disrupt data processing. Check data types with df.dtypes, validate inputs, and use try-except. Test in a sandbox, log errors with the logging module, and monitor with Sentry to ensure reliable DataFrame operations and stable data processing in production scripts.

28. Why does a NumPy array operation raise a ValueError?

ValueError in NumPy occurs due to shape mismatches or invalid data, breaking computations. Validate shapes with np.shape, ensure type compatibility, and test in a sandbox. Log errors with the logging module, and monitor with Sentry to prevent operation failures and ensure reliable numerical computations in production.

29. How do you optimize a Flask script for high traffic?

Optimizing Flask ensures scalable scripts. Use Gunicorn for WSGI serving, enable async routes with asyncio, and cache with Redis. Test with Locust in a sandbox to simulate load, automate tests with pytest, and monitor with Prometheus to ensure reliable, scalable performance in production environments.

30. When does a Django script fail database queries?

Django query failures occur due to incorrect ORM syntax or connection issues, disrupting data access. Validate models.py, check settings.py, and test in a sandbox. Log errors with the logging module, automate with pytest, and monitor with Sentry to ensure reliable database operations in production scripts.

31. Where do you store library dependencies for a Python script?

Library dependencies ensure consistent script execution.

• Generate requirements.txt with pip freeze.
• Commit to Git, excluding sensitive data.
• Automate updates with pipenv for consistency.
• Test compatibility in a sandbox environment.
• Monitor with Sentry for runtime errors.
  This ensures reliable dependency management across environments.

32. Which libraries improve Python script performance?

• Pandas: Efficient for structured data processing.
• NumPy: Optimized for numerical computations.
• Dask: Scales large dataset processing.
• Polars: High-performance DataFrame operations.
• Joblib: Parallelizes tasks effectively.
  These libraries enhance script performance. Test in a sandbox and monitor with Prometheus for reliability.

33. Who manages library updates for Python scripts?

Developers manage library updates, updating requirements.txt in Git. They test compatibility in a sandbox, automate with pipenv, and log with the logging module. Sentry monitoring ensures reliable library performance, preventing dependency-related issues in production scripts for team projects.

34. What causes a script to fail with a missing library?

Missing library errors halt execution due to uninstalled dependencies. Verify requirements.txt, install with pip, and test in a sandbox. Automate with pipenv, log errors with the logging module, and monitor with Sentry to ensure reliable library availability in production scripts.

35. Why does a FastAPI script return incorrect responses?

Incorrect FastAPI responses stem from validation errors or logic issues. Use Pydantic for validation, debug with print statements, and test with pytest. Log errors with the logging module, and monitor with Sentry to ensure accurate API responses and stable performance in production scripts.

36. How do you implement logging in a Python script?

import logging
logging.basicConfig(filename='script.log', level=logging.INFO, format='%(asctime)s - %(message)s')
def process_data(data):
logging.info(f"Processing: {data}")
try:
return data.upper()
except AttributeError as e:
logging.error(f"Error: {e}")
return None
Test in a sandbox, automate with pytest, and monitor with Sentry for reliable debugging.

37. What do you do when a script fails to load a library?

Library load failures disrupt scripts due to missing dependencies. Check requirements.txt, install with pip, and verify environment setup. Test in a sandbox, automate with pipenv, and monitor with Sentry to prevent library-related errors and ensure stable execution in production scripts.

38. Why does a Pandas script fail with a MemoryError?

MemoryError in Pandas occurs with large datasets, exhausting resources. Use chunking with pd.read_csv(chunksize=1000), optimize types with df.astype(), and test in a sandbox. Log memory usage with the logging module, automate with pytest, and monitor with Prometheus for efficient memory handling in production.

39. How do you integrate a Python script with a REST API?

import requests
def fetch_data(user_id):
try:
response = requests.get(f"https://api.example.com/users/{user_id}")
response.raise_for_status()
logging.info(f"Fetched user {user_id}")
return response.json()
except requests.RequestException as e:
logging.error(f"API error: {e}")
return None
Test in a sandbox, automate with pytest, and monitor with Sentry for reliability.

Error Handling

40. What do you do when a Python script crashes with an unhandled exception?

Unhandled exceptions disrupt script stability. Add try-except blocks, log errors with the logging module, and test in a sandbox. Automate error handling tests with pytest, and monitor with Sentry to detect and resolve exceptions, ensuring reliable script execution in production environments.

41. Why does a script fail to handle file I/O errors?

File I/O errors occur from missing files or permissions, halting scripts. Use try-except with IOError, validate paths, and test in a sandbox. Log errors with the logging module, automate with pytest, and monitor with Sentry to ensure robust file handling in production scripts.

42. How do you implement retry logic for network requests?

import requests
from time import sleep
def fetch_data(url, retries=3):
for attempt in range(retries):
try:
response = requests.get(url)
response.raise_for_status()
logging.info(f"Request succeeded")
return response.json()
except requests.RequestException as e:
logging.error(f"Attempt {attempt+1}: {e}")
sleep(2 ** attempt)
return None
Test in a sandbox, automate with pytest, and monitor with Sentry.

43. When does a script fail due to memory errors?

Memory errors occur with large datasets or inefficient code, exhausting resources. Profile with memory_profiler, use generators, and optimize data structures. Test in a sandbox, log with the logging module, automate with pytest, and monitor with Prometheus to ensure efficient memory usage in production scripts.

44. Where do you log errors for script debugging?

Error logging is essential for debugging scripts.

• Use logging.basicConfig for file-based logging.
• Export logs to CloudWatch for analysis.
• Monitor with Sentry for real-time alerts.
• Test logging in a sandbox environment.
• Automate log exports with scripts.
  This ensures effective error tracking in production scripts.

45. Which techniques improve error handling in scripts?

• Try-Except Blocks: Catch specific exceptions.
• Logging Module: Record detailed error data.
• Sentry: Monitor errors in real-time.
• Pytest: Test error handling logic.
• Context Managers: Ensure resource cleanup.
  These techniques ensure robust error handling. Test in a sandbox and monitor with Sentry for reliability.

46. Who implements error handling in Python scripts?

Developers implement error handling, adding try-except blocks and logging in Git modules. They test with pytest, automate error tracking, and monitor with Sentry to ensure robust, reliable scripts in production environments for team projects.

47. What causes a TypeError in a Python script?

TypeError results from incompatible operations, like adding strings and integers. Validate types with isinstance(), use try-except, and test in a sandbox. Log errors with the logging module, automate with pytest, and monitor with Sentry to prevent type-related failures in production scripts.

48. Why does a script fail to handle API errors?

API error handling fails due to uncaught exceptions, impacting reliability. Use try-except with requests.exceptions, validate status codes, and test in a sandbox. Log errors with the logging module, automate with pytest, and monitor with Sentry to ensure robust API interactions in production scripts.

49. How do you handle database connection errors in a script?

import psycopg2
def connect_db():
try:
conn = psycopg2.connect(dbname="mydb", user="user", password="pass")
logging.info("Database connected")
return conn
except psycopg2.Error as e:
logging.error(f"Database error: {e}")
return None
Test in a sandbox, automate with pytest, and monitor with Sentry for reliability.

50. What do you do when a script fails due to a ZeroDivisionError?

ZeroDivisionError halts scripts when dividing by zero. Check divisors before operations, use try-except, and test in a sandbox. Log errors with the logging module, automate with pytest, and monitor with Sentry to prevent division-related failures and ensure stable script execution in production.

51. Why does a script fail to handle timeout errors?

Timeout errors occur from slow servers, disrupting network operations. Use requests.get(timeout=5), handle with try-except, and test in a sandbox. Log timeouts with the logging module, automate retries with pytest, and monitor with Sentry to ensure robust network handling in production scripts.

52. How do you implement custom exception handling?

class CustomError(Exception):
pass
def process_data(data):
try:
if not data:
raise CustomError("Empty data")
logging.info(f"Processing: {data}")
return data.upper()
except CustomError as e:
logging.error(f"Custom error: {e}")
return None
Test in a sandbox, automate with pytest, and monitor with Sentry.

Integrations and APIs

53. What do you do when a script fails to connect to an API?

API connection failures disrupt data retrieval. Check URLs and keys, use try-except, and test in a sandbox. Log errors with the logging module, automate with pytest, and monitor with Sentry to ensure reliable API connections and stable script performance in production environments.

54. Why does a script fail to parse API responses?

Unexpected response formats cause parsing failures. Validate with json.loads() in try-except, test in a sandbox, and log errors with the logging module. Automate tests with pytest and monitor with Sentry to ensure reliable response parsing and script stability in production.

55. How do you integrate a script with a REST API?

import requests
def get_data(user_id):
try:
response = requests.get(f"https://api.example.com/users/{user_id}")
response.raise_for_status()
logging.info(f"Fetched user {user_id}")
return response.json()
except requests.RequestException as e:
logging.error(f"API error: {e}")
return None
Test in a sandbox, automate with pytest, and monitor with Sentry.

56. When does a script fail to connect to a database?

Database connection failures occur due to incorrect credentials or network issues. Validate connection strings, use try-except, and test in a sandbox. Log errors with the logging module, automate with pytest, and monitor with Sentry to ensure reliable database connections in production scripts.

57. Where do you store API credentials for a script?

API credentials are stored securely in environment variables.

• Use python-dotenv to load .env files.
• Exclude .env from Git commits.
• Automate credential loading with scripts.
• Test in a sandbox for reliability.
• Monitor with Sentry for access errors.
  This ensures secure credential management.

58. Which libraries improve API integration in scripts?

• Requests: Simplifies HTTP requests.
• HTTPx: Supports async API calls.
• Pydantic: Validates API responses.
• Aiohttp: Enables async request handling.
• Sentry: Monitors integration errors.
  These libraries enhance API performance. Test in a sandbox and monitor with Sentry.

59. Who manages API integrations in a Python script?

Developers manage API integrations, configuring libraries like Requests, testing in a sandbox, and automating with pytest. They log errors with the logging module and monitor with Sentry to ensure reliable API interactions and prevent failures in production scripts.

60. What causes authentication errors in a script’s API calls?

Invalid or expired API keys cause authentication errors. Validate credentials in .env, use try-except, and test in a sandbox. Log errors with the logging module, automate with pytest, and monitor with Sentry to ensure secure, reliable authentication in production scripts.

61. Why does a script fail to handle large API responses?

Large API responses cause memory issues or timeouts. Use requests.get(stream=True), process incrementally, and test in a sandbox. Log memory usage with the logging module, automate with pytest, and monitor with Prometheus to ensure efficient response handling in production.

62. How do you integrate a script with MySQL?

import mysql.connector
def fetch_data():
try:
conn = mysql.connector.connect(host="localhost", user="user", password="pass", database="mydb")
cursor = conn.cursor()
cursor.execute("SELECT * FROM users")
return cursor.fetchall()
except mysql.connector.Error as e:
logging.error(f"Database error: {e}")
return None
Test in a sandbox, automate with pytest, and monitor with Sentry.

63. What do you do when a script fails database authentication?

Database authentication failures occur due to incorrect credentials. Verify connection strings, check permissions, and test in a sandbox. Log errors with the logging module, automate with pytest, and monitor with Sentry to ensure reliable database authentication in production scripts.

64. Why does a script fail to handle database transaction errors?

Transaction errors arise from uncommitted changes or deadlocks. Use try-except with rollback, validate transaction logic, and test in a sandbox. Log errors with the logging module, automate with pytest, and monitor with Sentry to ensure robust transaction handling in production.

65. How do you implement retry logic for database connections?

import psycopg2
from time import sleep
def connect_db(retries=3):
for attempt in range(retries):
try:
conn = psycopg2.connect(dbname="mydb", user="user", password="pass")
logging.info("Database connected")
return conn
except psycopg2.Error as e:
logging.error(f"Attempt {attempt+1}: {e}")
sleep(2 ** attempt)
return None
Test in a sandbox, automate with pytest, and monitor with Sentry.

Testing and Debugging

66. What do you do when a test suite fails unexpectedly?

Unexpected test failures disrupt code validation. Check pytest logs, validate assertions, and debug with pdb. Test fixes in a sandbox, automate with pytest to prevent regressions, and monitor with Sentry to resolve test failures, ensuring reliable script performance in production environments.

67. Why does a unit test miss edge cases in a script?

Incomplete test coverage causes edge case failures, leading to bugs. Add edge case tests with pytest, validate inputs, and debug with pdb. Test in a sandbox, log errors with the logging module, and monitor with Sentry to ensure comprehensive testing in production scripts.

68. How do you implement unit tests for a script function?

import pytest
def add_numbers(a, b):
return a + b
def test_add_numbers():
assert add_numbers(2, 3) == 5
assert add_numbers(-1, 1) == 0
with pytest.raises(TypeError):
add_numbers("2", 3)
logging.info("Tests passed")
Test in a sandbox, automate with pytest, and monitor with Sentry.

69. When does a script require additional debugging?

Additional debugging is needed for intermittent failures or complex logic errors. Use pdb for step-through debugging, log with the logging module, and test in a sandbox. Automate with pytest and monitor with Sentry to ensure reliable debugging and stable script execution in production.

70. Where do you store test cases for a Python script?

Test cases are stored in a tests directory in a Git repository.

• Create test_module.py for each module.
• Commit to GitHub or Bitbucket.
• Automate tests with pytest.
• Monitor with Sentry for failures.
• Test in a sandbox environment.
  This ensures organized, reliable testing.

71. Which tools improve script testing and debugging?

• Pytest: Streamlines unit testing.
• Unittest: Built-in testing framework.
• Pdb: Interactive debugging tool.
• Coverage.py: Measures test coverage.
• Sentry: Monitors test failures.
  These tools enhance testing efficiency. Test in a sandbox and monitor with Sentry for reliability.

72. Who writes unit tests for Python scripts?

Developers write unit tests, storing them in a Git tests directory. They automate with pytest, test in a sandbox, and log with the logging module. Sentry monitoring ensures test failure detection, maintaining robust and reliable scripts in production environments.

73. What causes intermittent test failures in a script?

Intermittent test failures stem from race conditions or dependencies. Stabilize environments with mocks, test with pytest, and debug with pdb. Log errors with the logging module, automate tests, and monitor with Sentry to ensure reliable test execution in production scripts.

74. Why does a script fail during debugging?

Debugging failures occur from incorrect breakpoints or logic errors. Use pdb.set_trace(), validate logic, and test in a sandbox. Log errors with the logging module, automate with pytest, and monitor with Sentry to ensure effective debugging and stable script execution.

75. How do you mock external APIs in script tests?

import pytest
from unittest.mock import patch
def get_data(user_id):
response = requests.get(f"")
return response.json()
def test_get_data():
with patch('requests.get') as mocked_get:
mocked_get.return_value.json.return_value = {'id': 1}
assert get_data(1)['id'] == 1
Test in a sandbox, automate with pytest, and monitor with Sentry.

Concurrency and Performance

76. What do you do when a script runs slowly with large datasets?

Slow scripts impact performance. Profile with cProfile, use generators, and optimize loops. Test in a sandbox, automate with pytest, and monitor with Prometheus to ensure efficient execution and prevent slowdowns in production scripts handling large datasets.

77. Why does a script fail in multithreading scenarios?

Multithreading failures occur due to the GIL limiting CPU-bound tasks. Use multiprocessing for CPU tasks, test in a sandbox, and log with the logging module. Automate with pytest and monitor with Sentry to ensure reliable concurrent execution in production scripts.

78. How do you implement async I/O in a Python script?

import asyncio
import aiohttp
async def fetch_data(url):
async with aiohttp.ClientSession() as session:
async with session.get(url) as response:
return await response.json()
loop = asyncio.get_event_loop()
loop.run_until_complete(fetch_data('https://api.example.com'))
Test in a sandbox, automate with pytest, and monitor with Sentry.

79. When does a script need multiprocessing?

Multiprocessing is needed for CPU-bound tasks like data processing. Use multiprocessing.Pool, test in a sandbox, and log with the logging module. Automate with pytest and monitor with Prometheus to ensure efficient parallel execution in production scripts.

80. Where do you optimize script performance?

Performance optimizations are applied in critical code paths.

• Profile with cProfile for bottlenecks.
• Use generators for memory efficiency.
• Automate performance tests with pytest.
• Monitor with Prometheus for metrics.
• Test in a sandbox environment.
  This ensures efficient, scalable scripts.

81. Which tools improve script performance?

• CProfile: Identifies performance bottlenecks.
• PyPy: Faster Python interpreter.
• Numba: Accelerates numerical code.
• Multiprocessing: Parallelizes tasks.
• Prometheus: Monitors performance metrics.
  These tools enhance script performance. Test in a sandbox and monitor with Prometheus.

82. Who optimizes Python script performance?

Developers optimize script performance, profiling with cProfile, and testing in a sandbox. They automate with pytest, log with the logging module, and monitor with Prometheus to ensure efficient, reliable performance in production scripts for team projects.

83. What causes a script to deadlock in multithreading?

Deadlocks occur from resource contention or improper locks. Use threading.Lock with timeouts, test in a sandbox, and log with the logging module. Automate with pytest and monitor with Sentry to prevent deadlocks in production scripts.

84. Why does an async function fail in a script?

Async failures result from incorrect await usage. Validate async/await syntax, test with asyncio.run(), and log with the logging module. Automate with pytest and monitor with Sentry for reliable asynchronous execution in production scripts.

85. How do you profile a script for performance?

import cProfile
def compute_sum(n):
total = 0
for i in range(n):
total += i
return total
cProfile.run('compute_sum(1000000)')
Optimize bottlenecks, test in a sandbox, automate with pytest, and monitor with Prometheus for performance.

Automation and Scripting

86. What do you do when a Python script fails in production automation?

Production automation failures disrupt workflows. Check logs with the logging module, debug with pdb, and test fixes in a sandbox. Automate with pytest to prevent regressions, and monitor with Sentry to resolve issues, ensuring reliable automation in production environments.

87. Why does a script fail to scale with user load?

Scaling failures occur from inefficient algorithms. Optimize with generators, cache with Redis, and test with Locust. Log with the logging module, automate with pytest, and monitor with Prometheus to ensure scalable performance in production automation scripts.

88. How do you build a script for file processing automation?

def process_file(file_path):
try:
with open(file_path, 'r') as file:
data = file.read().splitlines()
logging.info(f"Processed {len(data)} lines")
return data
except IOError as e:
logging.error(f"Error: {e}")
return None
Test in a sandbox, automate with pytest, and monitor with Sentry.

89. When does a script need optimization for large files?

Large file processing slows scripts due to memory usage. Use generators for incremental reading, test in a sandbox, and log with the logging module. Automate with pytest and monitor with Prometheus to ensure efficient file processing in production scripts.

90. Where do you deploy automation scripts?

Automation scripts are deployed in scalable environments.

• Use Docker for containerized execution.
• Deploy to AWS Lambda for serverless.
• Automate with CI/CD pipelines.
• Monitor with Prometheus for metrics.
• Test in a sandbox environment.
  This ensures reliable automation in production.

91. Which tools support Python automation scripts?

• Schedule: Runs periodic tasks.
• Celery: Manages distributed tasks.
• Airflow: Orchestrates workflows.
• Docker: Containerizes scripts.
• Prometheus: Monitors performance.
  These tools ensure efficient automation. Test in a sandbox and monitor with Prometheus.

92. Who maintains automation scripts in a team?

Developers maintain automation scripts, storing them in Git. They test with pytest, automate with CI/CD, log with the logging module, and monitor with Prometheus to ensure reliable automation in production environments for team projects.

93. What causes a script to fail during data migration?

Data migration failures stem from schema mismatches. Validate schemas, use try-except, and test in a sandbox. Log with the logging module, automate with pytest, and monitor with Sentry to ensure reliable migrations in production scripts.

94. Why does a script fail to process real-time data?

Real-time data failures occur from latency or resource issues. Use asyncio for async processing, test in a sandbox, and log with the logging module. Automate with pytest and monitor with Prometheus for reliable real-time processing.

95. How do you implement a script for web scraping?

import requests
from bs4 import BeautifulSoup
def scrape_page(url):
try:
response = requests.get(url)
soup = BeautifulSoup(response.text, 'html.parser')
return [item.text for item in soup.find_all('p')]
except requests.RequestException as e:
logging.error(f"Error: {e}")
return None
Test in a sandbox, automate with pytest, and monitor with Sentry.

Security and Compliance

96. What do you do when a script exposes sensitive data?

Sensitive data exposure risks security breaches. Use environment variables for secrets, scan with Bandit, and test in a sandbox. Log errors with the logging module, excluding sensitive data, and monitor with Sentry to ensure secure script execution in production.

97. Why does a script fail security compliance checks?

Compliance failures occur from insecure dependencies. Scan with pip-audit, update packages, and test in a sandbox. Log vulnerabilities with the logging module, automate with CI/CD, and monitor with Sentry to ensure compliant, secure scripts in production.

98. How do you secure API credentials in a script?

import os
from dotenv import load_dotenv
load_dotenv()
api_key = os.getenv('API_KEY')
def call_api():
try:
response = requests.get('https://api.example.com', headers={'Authorization': api_key})
return response.json()
except requests.RequestException as e:
logging.error(f"Error: {e}")
return None
Test in a sandbox, automate with pytest, and monitor with Sentry.

99. When does a script require security auditing?

Security auditing is needed for sensitive data handling. Use Bandit for code analysis, test in a sandbox, and log with the logging module. Automate scans with CI/CD and monitor with Sentry to ensure secure, compliant script execution in production.

100. Where do you store security configurations?

Security configurations are stored in environment variables.

• Load with python-dotenv from .env files.
• Exclude .env from Git commits.
• Automate configuration loading.
• Test in a sandbox environment.
• Monitor with Sentry for errors.
  This ensures secure configuration management.

101. Which tools enhance script security?

• Bandit: Scans for vulnerabilities.
• Pip-audit: Checks dependency security.
• Python-dotenv: Secures credentials.
• Secrets: Detects sensitive data.
• Sentry: Monitors runtime errors.
  These tools ensure secure scripts. Test in a sandbox and monitor with Sentry.