Most Asked Python Interview Questions [2025 Updated]

This guide offers 102 frequently asked Python interview questions with detailed answers, designed for Python Engineer roles in data engineering, web development, AI/ML, and cloud-based development. Covering core Python concepts, frameworks like Django and Flask, CI/CD integration, and AWS services, it emphasizes practical applications and modern trends like serverless architectures and AI-driven pipelines. This guide equips candidates to excel in technical interviews for Python-centric roles.

Python Core Concepts

1. Why is Python a top choice for engineering roles?

Python’s simplicity, versatility, and robust ecosystem make it ideal for engineering tasks. Its readable syntax accelerates development for web, data, and AI/ML applications. Libraries like Pandas, TensorFlow, and Boto3 support diverse workloads, while AWS integration enables scalable CI/CD pipelines. Python’s cross-platform nature ensures flexibility for cloud-native solutions, a critical topic for Python interviews.

2. What are Python’s essential features for developers?

Python provides key features enhancing its utility in engineering roles.

• Clear Syntax: Simplifies code maintenance for large projects.
• Interpreted Execution: Facilitates debugging in CI/CD workflows.
• Dynamic Typing: Speeds prototyping by eliminating type declarations.
• Rich Libraries: Supports data engineering and AI/ML with NumPy and Scikit-learn.
• Portability: Ensures seamless deployment across platforms like AWS.
  These features drive Python’s adoption, a frequent interview discussion.

3. How does Python compare to Java or C++ in engineering?

Python prioritizes simplicity and rapid development, unlike Java’s strict typing or C++’s low-level control. Its interpreted nature enables faster prototyping for data engineering compared to Java’s compiled approach. Python’s dynamic typing reduces complexity, unlike C++’s static typing, making it ideal for CI/CD and AI/ML, though less performant for CPU-intensive tasks, a key interview comparison.

4. What is the Global Interpreter Lock (GIL) in Python?

The Global Interpreter Lock (GIL) in CPython synchronizes thread execution, preventing concurrent access to Python objects. It simplifies memory management but limits multi-threading for CPU-bound tasks. For I/O-bound CI/CD or web tasks, the GIL is less restrictive, but multiprocessing is preferred for parallel processing, a critical concept for Python interviews.

5. How do Python lists differ from tuples?

Lists are mutable, allowing dynamic updates like appending elements, ideal for data engineering tasks, while tuples are immutable, ensuring data integrity for fixed datasets. Lists consume more memory with methods like append(), while tuples offer faster iteration. Lists suit CI/CD data, while tuples are used for configurations, a common Python interview topic.

6. What are Python dictionaries, and when are they used?

Python dictionaries are mutable key-value stores with O(1) lookup times, ideal for data engineering tasks like caching API responses or storing CI/CD configurations. They enable efficient data retrieval in web development or analytics, offering flexibility for dynamic datasets, a key data structure for Python interviews.

7. What is a Python set, and what are its use cases?

A Python set is an unordered collection of unique elements, optimized for operations like union or intersection. It’s used in data engineering for deduplicating CI/CD logs or filtering unique IDs in analytics. Sets provide efficient membership testing, making them valuable for large-scale data processing, a frequent Python interview topic.

8. How does Python handle memory management?

Python uses automatic memory management with reference counting and a generational garbage collector. Objects are allocated on the heap, and memory is reclaimed when references reach zero. Cyclic references are resolved by the garbage collector, ensuring efficient memory use in data engineering and AI/ML, a critical Python interview concept.

9. What is dynamic typing, and why is it useful in Python?

Dynamic typing allows variables to change types without declarations, enhancing flexibility in prototyping CI/CD or data engineering scripts. It simplifies code but requires robust error handling to avoid runtime issues. This feature accelerates development in agile environments, a common Python interview discussion point.

10. What are Python generators, and how do they optimize memory?

Generators yield values one at a time using yield, reducing memory usage for large datasets in data engineering. They enable lazy evaluation, ideal for streaming CI/CD logs or processing big data with Pandas, optimizing performance in memory-constrained environments, a key Python interview topic.

11. How do list comprehensions enhance Python coding?

List comprehensions, like [x*2 for x in range(10)], provide concise, efficient syntax for creating lists, outperforming loops in data engineering tasks. They improve readability and performance in CI/CD pipelines or analytics, making them a frequently tested topic in Python interviews.

12. What are Python modules, and how are they organized?

Modules are Python files containing reusable code, imported using import. They organize CI/CD or data engineering projects, enabling modularity with libraries like os or custom modules. Proper module organization enhances scalability, a critical concept for Python interviews.

13. How does Python’s import system function?

The import system loads modules into the namespace, enabling access to functions or classes. Absolute and relative imports organize CI/CD codebases, while sys.path determines search paths. Understanding imports ensures modular, scalable applications, a common Python interview topic.

14. What is the difference between __str__ and __repr__?

__str__ returns a user-friendly string representation for print(), while __repr__ provides a detailed, developer-oriented representation for debugging. In CI/CD pipelines, __str__ aids logging, while __repr__ supports diagnostics, a key Python interview distinction.

15. How is inheritance implemented in Python?

Inheritance enables classes to inherit attributes and methods, promoting code reuse in web or data engineering. Single or multiple inheritance supports flexible designs, like extending Django models, demonstrating OOP proficiency, a critical Python interview topic.

16. What is the purpose of Python’s __init__ method?

The __init__ method initializes class instances, setting attributes for objects like Django models. It ensures proper setup for CI/CD-driven applications, enabling structured data handling, a fundamental concept for Python interviews.

17. How do Python’s setdefault and get methods work?

dict.get(key, default) retrieves a value or returns a default if the key is missing, while setdefault(key, default) sets a default value if the key doesn’t exist. These simplify dictionary operations in CI/CD pipelines, a common Python interview topic.

18. What is the difference between a list and an array in Python?

Lists are built-in, heterogeneous, and mutable, ideal for general CI/CD tasks, while arrays (from array module or NumPy) are homogeneous and optimized for numerical operations in data engineering. Arrays are more memory-efficient, a key Python interview distinction.

19. How does Python’s slice object work?

The slice object defines a range for indexing, like slice(1, 5, 2) for elements 1 to 4 with step 2. It’s used in data engineering for extracting dataset portions in CI/CD pipelines, enhancing code flexibility, a frequent Python interview topic.

20. What are Python’s built-in functions, and why are they useful?

Built-in functions like len(), map(), and filter() simplify tasks in CI/CD and data engineering. They optimize code by reducing loops, enhancing performance for data transformation or log processing, a critical Python interview topic.

Python Programming and Best Practices

21. How do you optimize Python code for performance?

Optimizing Python code enhances efficiency in engineering tasks.

• Built-in Functions: Use map() for faster iterations.
• List Comprehensions: Replace loops for concise processing.
• Profiling Tools: Leverage cProfile to identify CI/CD bottlenecks.
• Libraries: Use NumPy for vectorized operations.
• Caching: Implement memoization for repetitive tasks.
  These ensure scalable code, a key Python interview focus.

22. What are Python decorators, and how are they used?

Decorators modify function behavior, used for logging, authentication, or timing in CI/CD pipelines. They wrap functions to add functionality, like logging Flask API calls, demonstrating advanced Python skills, a frequent Python interview topic.

23. How do you handle exceptions in Python?

Python’s try-except blocks catch errors like ValueError, ensuring robust CI/CD pipelines. Specific exception handling prevents crashes, and finally ensures resource cleanup, like closing database connections, a critical best practice for Python interviews.

24. What is the difference between == and is operators?

== compares object values for equality, while is checks identity (same memory location). For example, [1, 2] == [1, 2] is true, but [1, 2] is [1, 2] is false. This is crucial for debugging CI/CD pipelines, a key Python interview topic.

25. How do you implement context managers in Python?

Context managers, using with, manage resources like files in CI/CD scripts. The __enter__ and __exit__ methods ensure setup and cleanup, preventing leaks, like with open('file.txt') as f:, a best practice for Python interviews.

26. What are *args and **kwargs in Python functions?

*args accepts variable positional arguments, and **kwargs handles variable keyword arguments, enhancing flexibility in CI/CD scripts. For example, *args collects logging inputs, and **kwargs passes configurations, a common Python interview topic.

27. How does Python support object-oriented programming?

Python supports OOP with classes, inheritance, polymorphism, and encapsulation. Classes define data models for Django apps, inheritance enables reuse, and encapsulation protects data, critical for scalable CI/CD projects, a key Python interview topic.

28. What is a lambda function, and when is it used?

Lambda functions are anonymous, single-expression functions for concise operations, like lambda x: x*2 in list comprehensions. They’re ideal for short-lived CI/CD tasks but less readable for complex logic, a frequent Python interview topic.

29. How do you manage dependencies in Python projects?

Dependency management uses pip and virtualenv to isolate environments, with requirements.txt listing CI/CD dependencies. Tools like poetry ensure reproducible builds in cloud-based Python apps, a critical best practice for Python interviews.

30. What is the collections module, and how is it used?

The collections module provides data structures like Counter for counting occurrences or deque for queue operations in CI/CD tasks. namedtuple enhances readability, making it valuable for data engineering, a common Python interview topic.

31. How do you handle file operations in Python?

File operations use open() with with statements to ensure closure, like with open('file.txt', 'r') as f:. This prevents resource leaks in CI/CD log processing or data engineering, a frequent Python interview scenario.

32. What is the zip function, and how is it used?

The zip function combines iterables into tuples, like zip([1, 2], ['a', 'b']) yielding (1, 'a'), (2, 'b'). It’s used in data engineering for pairing datasets in CI/CD pipelines, enhancing efficiency, a common Python interview topic.

33. How does Python’s deepcopy differ from shallowcopy?

copy.deepcopy() creates independent copies of objects and nested structures, while copy.copy() duplicates only the top level, sharing nested references. Deep copying ensures data integrity in CI/CD pipelines, a key Python interview distinction.

34. How do you implement iterators in Python?

Iterators use __iter__ and __next__ methods for custom iteration, like processing CI/CD logs. The itertools module provides utilities like cycle, enhancing data engineering tasks, a critical Python interview topic.

35. What is the functools module, and how is it used?

The functools module provides tools like partial for function customization or lru_cache for memoization in CI/CD tasks. It enhances code efficiency in data engineering or web apps, a common Python interview topic.

36. How do you implement custom exceptions in Python?

Custom exceptions are created by subclassing Exception, like class CustomError(Exception): pass. They handle specific errors in CI/CD pipelines, improving error clarity and robustness, a frequent Python interview topic.

37. What is the difference between __new__ and __init__?

__new__ creates a new instance, while __init__ initializes it. __new__ is used for custom object creation in CI/CD apps, while __init__ sets attributes, a key distinction for advanced Python interviews.

38. How do you use Python’s property decorator?

The property decorator creates getter and setter methods for class attributes, ensuring controlled access in CI/CD-driven apps. It simplifies data encapsulation, a common Python interview topic for OOP.

39. What is the enum module, and how is it used?

The enum module defines enumerated constants, improving code readability in CI/CD configurations. For example, Enum('Status', 'SUCCESS FAILURE') ensures consistent values, a niche Python interview topic.

40. How do you handle JSON data in Python?

Python’s json module serializes and deserializes JSON data, like json.loads() for parsing API responses in CI/CD pipelines. It ensures efficient data exchange in web or data engineering, a common Python interview topic.

Python Frameworks and Web Development

41. What is Django, and how does it support web development?

Django is a high-level Python framework for rapid, secure web development, offering ORM, authentication, and admin interfaces. It simplifies database interactions and API development for CI/CD-integrated web apps, a key topic for Python web interviews.

42. How does Flask differ from Django for web projects?

Flask is lightweight and flexible for microservices or small APIs, while Django’s comprehensive features suit complex web applications. Flask offers customization for CI/CD-driven APIs, while Django’s ORM streamlines development, a critical Python interview distinction.

43. How do you secure a Django application?

Securing Django involves:

• CSRF Protection: Enable tokens to prevent cross-site attacks.
• Authentication: Use built-in user authentication for secure logins.
• HTTPS: Enforce SSL/TLS for data encryption.
• Input Validation: Sanitize inputs to prevent SQL injection.
  These ensure secure CI/CD apps, a key Python interview topic.

44. What is the Django ORM, and why is it useful?

Django’s ORM abstracts database operations, enabling Python objects to interact with databases without SQL. It supports migrations for CI/CD pipelines, simplifying data modeling for web apps, a common Python interview topic.

45. How is Flask used for building REST APIs?

Flask creates lightweight REST APIs with routes handling HTTP methods like GET or POST. Extensions like Flask-RESTful enhance CI/CD-driven API development, supporting scalable microservices, a critical Python web interview topic.

46. What are Django migrations, and why are they important?

Django migrations manage database schema changes, generating SQL from model definitions. They ensure consistent database states in CI/CD environments, enabling seamless updates, a key Python interview topic for scalability.

47. How do you handle static files in Django?

Django serves static files using STATICFILES_DIRS and collectstatic for production. In CI/CD pipelines, files are stored in AWS S3 or CloudFront for scalability, ensuring efficient web app performance, a common Python interview topic.

48. What is WSGI, and how does it support Python web apps?

WSGI connects Python web apps to servers, with tools like Gunicorn enabling Django or Flask to handle HTTP requests in CI/CD deployments. It ensures scalability, a critical Python web interview topic.

49. How do you optimize Django application performance?

Django performance optimization includes:

• Query Optimization: Use select_related to reduce queries.
• Caching: Implement Redis for frequent data access.
• Load Balancing: Use AWS ELB for traffic distribution.
• Indexing: Add database indexes for faster lookups.
  These ensure scalable CI/CD apps, a key Python interview focus.

50. What is Flask’s Blueprint, and how is it used?

Flask Blueprints modularize large applications, organizing routes and views for maintainability. They enable separation of concerns in CI/CD-driven APIs, supporting scalable microservices, a critical Python web interview topic.

51. How do you handle authentication in Flask?

Flask uses Flask-Login or JWT for authentication in CI/CD-driven APIs. Token-based authentication secures endpoints, integrating with AWS Secrets Manager for credentials, a frequent Python interview topic.

52. What is Django’s middleware, and how is it used?

Django middleware processes requests and responses globally, enabling logging or authentication in web apps. Custom middleware in CI/CD pipelines adds functionality like request tracking, a key Python interview topic.

53. How do you implement RESTful routing in Django?

Django REST Framework (DRF) implements RESTful routing with viewsets and routers, mapping HTTP methods to CRUD operations. It integrates with CI/CD pipelines for automated API testing, a critical Python interview topic.

54. What is FastAPI, and how does it compare to Flask?

FastAPI is a modern Python framework for asynchronous APIs, offering high performance with asyncio. Unlike Flask’s synchronous approach, FastAPI supports concurrent requests for CI/CD-driven microservices, a common Python interview comparison.

55. How do you manage database connections in Django?

Django’s ORM pools connections, with settings like CONN_MAX_AGE optimizing performance in CI/CD pipelines. This ensures scalable web apps with minimal overhead, a key Python web interview topic.

Data Engineering and Python

56. How is Python used in data engineering pipelines?

Python powers data engineering with Pandas for data manipulation, PySpark for big data, and Airflow for orchestration. It integrates with AWS Glue for ETL in CI/CD pipelines, enabling scalable analytics, a critical Python interview topic.

57. What is Pandas, and how does it support data engineering?

Pandas provides DataFrames for data manipulation, supporting cleaning and analysis in CI/CD pipelines. It integrates with AWS S3 or Redshift, offering efficiency for data engineering tasks, a key Python interview topic.

58. How does PySpark enhance big data processing?

PySpark, Apache Spark’s Python API, processes large datasets in distributed environments. It supports ETL and analytics, integrating with AWS EMR for CI/CD pipelines, a frequent Python data engineering interview topic.

59. What is Apache Airflow, and how does Python use it?

Apache Airflow, a Python-based tool, orchestrates data pipelines with DAGs. It automates ETL in CI/CD environments, integrating with AWS S3 or Redshift, a critical Python interview topic.

60. How do you optimize Pandas for large datasets?

Optimizing Pandas includes:

• Chunking: Process data in batches to reduce memory.
• Data Types: Use float32 for efficiency.
• Vectorization: Leverage NumPy for loop-free operations.
• Parallelization: Use Dask for distributed computing.
  These ensure scalable CI/CD pipelines, a key Python interview topic.

61. What is NumPy, and why is it critical for data engineering?

NumPy provides efficient array operations, outperforming Python loops for numerical computations in CI/CD or AI/ML preprocessing. Its vectorized operations enhance performance, a common Python interview topic.

62. How do you handle missing data in Pandas?

Pandas uses dropna() to remove nulls or fillna() to impute values like means. These ensure data integrity in CI/CD-driven analytics pipelines, a frequent Python interview scenario.

63. What is AWS Glue, and how does Python integrate with it?

AWS Glue is a serverless ETL service using Python scripts to transform data in S3 or Redshift. It automates CI/CD-driven analytics, a key Python data engineering interview topic.

64. How do you process large CSV files in Python?

Large CSV files are processed with Pandas chunking or Dask for out-of-memory datasets, integrating with CI/CD pipelines for scalable data engineering, a common Python interview topic.

65. What is SQLAlchemy, and how is it used in Python?

SQLAlchemy is a Python ORM abstracting SQL queries, simplifying database interactions in CI/CD pipelines. It integrates with AWS RDS, supporting scalable data engineering, a key Python interview topic.

66. How do you perform data validation in Python?

Data validation uses Pandas for schema checks, Pydantic for type validation, or custom scripts to ensure data quality in CI/CD pipelines, preventing errors in analytics, a critical Python interview topic.

67. What is Dask, and how does it support big data?

Dask scales Python data processing for large datasets, parallelizing Pandas or NumPy operations. It integrates with CI/CD pipelines for distributed analytics, a frequent Python interview topic.

68. How do you integrate Python with Apache Kafka?

Python uses confluent-kafka to stream data in CI/CD pipelines with Kafka. Producers send data, and consumers process it for real-time analytics, a key Python interview topic.

69. What is the role of Python in ETL processes?

Python automates ETL with Pandas for transformation, SQLAlchemy for extraction, and AWS Glue for loading data into S3 or Redshift, streamlining CI/CD pipelines, a critical Python interview topic.

70. How do you visualize data in Python?

Data visualization uses Matplotlib for plots, Seaborn for statistical graphics, or Plotly for interactive visuals in CI/CD pipelines, enhancing analytics insights, a common Python interview topic.

AI/ML and Python Development

71. How is Python used in AI/ML development?

Python excels in AI/ML with TensorFlow, PyTorch, and Scikit-learn for model building. It integrates with AWS SageMaker for CI/CD-driven ML pipelines, enabling scalable analytics, a key Python interview topic.

72. What is TensorFlow, and how is it applied in Python?

TensorFlow is an open-source Python library for neural networks, supporting CI/CD-integrated model training with AWS SageMaker. It ensures scalable AI/ML applications, a frequent Python interview topic.

73. How does Scikit-learn support machine learning in Python?

Scikit-learn provides tools for classification, regression, and clustering, simplifying preprocessing in CI/CD pipelines. It integrates with AWS for scalable ML, a key Python interview topic.

74. What is PyTorch, and how does it compare to TensorFlow?

PyTorch offers flexibility with dynamic graphs for AI/ML research, while TensorFlow focuses on production-ready CI/CD pipelines. PyTorch’s ease contrasts with TensorFlow’s robustness, a common Python interview comparison.

75. How do you preprocess data for AI/ML models in Python?

Preprocessing uses Pandas for cleaning, NumPy for numerical operations, and Scikit-learn for scaling, ensuring data quality in CI/CD-driven ML pipelines with AWS SageMaker, a critical Python interview topic.

76. What is AWS SageMaker, and how does Python integrate?

AWS SageMaker builds and deploys ML models using Python scripts, integrating with CI/CD pipelines via CodePipeline for scalable ML workflows, a key Python cloud interview topic.

77. How do you evaluate ML models in Python?

Model evaluation uses Scikit-learn metrics like accuracy or RMSE, with cross-validation for robustness in CI/CD pipelines. Confusion matrices visualize performance, a critical Python interview topic.

78. What is overfitting, and how is it prevented in Python?

Overfitting occurs when models fit training data too closely. Prevention includes regularization, cross-validation, and dropout in TensorFlow, ensuring robust CI/CD-driven models, a key Python interview topic.

79. How do you deploy ML models using Python?

ML models deploy using Flask or FastAPI for APIs, with AWS Lambda or SageMaker for CI/CD automation. Docker ensures scalability, a critical Python interview topic.

80. What is Keras, and how is it used in Python?

Keras is a high-level API for neural networks, used with TensorFlow for CI/CD-driven AI/ML pipelines. It simplifies model creation, integrating with AWS SageMaker, a common Python interview topic.

81. How do you handle imbalanced datasets in Python?

Imbalanced datasets are managed with Scikit-learn’s SMOTE, undersampling, or class weighting, ensuring robust ML models in CI/CD pipelines, a key Python AI/ML interview topic.

82. What is XGBoost, and how is it used in Python?

XGBoost is a gradient boosting library for high-performance ML, integrating with CI/CD pipelines for scalable classification or regression, a frequent Python interview topic.

83. How do you implement feature engineering in Python?

Feature engineering uses Pandas for creating features, Scikit-learn for encoding, and NumPy for transformations, enhancing ML model performance in CI/CD pipelines, a critical Python interview topic.

84. What is NLTK, and how is it used in Python?

NLTK is a Python library for NLP, supporting tokenization or sentiment analysis in CI/CD-driven text processing, a key Python AI/ML interview topic.

85. How do you optimize ML model training in Python?

Optimizing ML training uses efficient algorithms, hyperparameter tuning with GridSearchCV, and AWS SageMaker for distributed training, reducing CI/CD pipeline time, a critical Python interview topic.

Cloud and CI/CD Integration with Python

86. How does Python integrate with AWS for CI/CD?

Python integrates with AWS CodePipeline, CodeBuild, and Lambda using Boto3 to automate CI/CD workflows, managing resources like S3 or EC2 for scalable cloud-native pipelines, a critical Python interview topic.

87. What is Boto3, and how is it used in AWS?

Boto3, the AWS SDK for Python, enables programmatic access to S3, EC2, or Lambda, automating CI/CD tasks like artifact uploads or scaling, a key Python cloud interview topic.

88. How do you automate AWS tasks with Python?

Python automates AWS tasks with Boto3, managing S3 buckets, EC2 instances, or Lambda functions in CI/CD pipelines, ensuring scalable workflows, a critical Python interview skill.

89. What is AWS Lambda, and how does Python support it?

AWS Lambda runs Python functions for CI/CD events like S3 uploads, with Boto3 enabling cost-efficient automation, a key Python cloud engineering interview topic.

90. How do you secure Python applications on AWS?

Securing Python apps involves:

• IAM Roles: Enforce least-privilege access for Lambda or EC2.
• Encryption: Use KMS for S3 or RDS data security.
• VPC: Isolate CI/CD resources.
• Secrets Manager: Store credentials securely.
  These ensure compliance, a critical Python interview topic.

91. How do you monitor Python applications on AWS?

Monitoring uses CloudWatch for metrics, X-Ray for CI/CD tracing, and CloudTrail for API audits. Python scripts parse logs, ensuring observability, a key Python interview topic.

92. What is AWS CodePipeline, and how does Python integrate?

AWS CodePipeline automates CI/CD workflows, with Python scripts in CodeBuild or Lambda handling build tasks, integrating with S3 for artifacts, a critical Python interview topic.

93. How do you use Python with AWS S3?

Python uses Boto3 to interact with S3 for CI/CD artifacts or ML data, managing bucket policies and file transfers, a common Python cloud interview topic.

94. What is AWS Glue, and how does Python enhance it?

AWS Glue is a serverless ETL service using Python scripts for data transformation in S3 or Redshift, automating CI/CD-driven analytics, a key Python interview topic.

95. How do you deploy Python applications on AWS?

Python apps deploy using Elastic Beanstalk, ECS, or Lambda, with CodePipeline automating CI/CD, integrating with S3 and CloudWatch, a critical Python interview topic.

Testing and Debugging in Python

96. How do you write unit tests in Python?

Unit tests use unittest or pytest to verify Python code in CI/CD pipelines, with unittest.mock mocking dependencies, ensuring robust apps, a critical Python interview topic.

97. What is pytest, and why is it preferred?

Pytest offers simple syntax and features like fixtures, simplifying test discovery in CI/CD pipelines. It’s preferred over unittest for scalability, a common Python interview topic.

98. How do you debug Python code?

Debugging uses pdb, logging, or PyCharm breakpoints. In CI/CD pipelines, CloudWatch logs or print statements identify issues, ensuring robust apps, a critical Python interview skill.

99. What is mocking in Python testing?

Mocking with unittest.mock simulates dependencies in CI/CD tests, isolating code for reliable testing without external calls, a key Python interview topic.

100. How do you test Python APIs?

Testing APIs uses pytest with requests to verify HTTP responses in CI/CD pipelines, with mocking for external services, a common Python interview scenario.

101. How do you ensure code coverage in Python?

Code coverage uses pytest-cov to measure tested code in CI/CD pipelines, with tools like Coveralls integrating with AWS CodeBuild, a critical Python interview topic.

102. How do you prepare for Python Engineer interviews?

Preparation involves:

• Coding Practice: Solve LeetCode problems for algorithms.
• Projects: Build CI/CD pipelines with Flask or Pandas.
• Frameworks: Master Django, Airflow, or FastAPI.
• Cloud Skills: Use Boto3 for AWS automation.
• Resources: Study Python docs and AWS Skill Builder.
  Interviews test Python proficiency, cloud integration, and problem-solving, ensuring readiness for engineering roles.