Python Engineer Interview Questions with Answers [2025]

This comprehensive guide provides 102 Python Engineer interview questions with expert answers, tailored for candidates preparing for roles requiring expertise in Python for 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 with insights to excel in technical interviews for Python engineering roles.

Python Core Concepts

1. Why is Python a preferred language for engineering roles?

Python’s versatility, readable syntax, and extensive ecosystem make it a top choice for engineering tasks. Its simplicity accelerates development for web applications, data pipelines, and AI/ML models. Libraries like NumPy, Pandas, and TensorFlow support diverse workloads, while integration with AWS for CI/CD pipelines ensures scalability and automation, making Python ideal for cloud-native engineering solutions.

2. What are the key features of Python for developers?

Python offers critical features enhancing its utility in engineering roles.

• Readable Syntax: Clear code improves maintainability for complex projects.
• Interpreted Nature: Line-by-line execution simplifies debugging in CI/CD workflows.
• Dynamic Typing: Eliminates type declarations, speeding up prototyping.
• Extensive Libraries: Supports data engineering and AI/ML with Pandas and Scikit-learn.
• Cross-Platform Compatibility: Ensures seamless deployment on AWS or local systems.
  These features drive Python’s adoption in scalable engineering.

3. How does Python differ from Java or C++ in engineering?

Python emphasizes 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 bytecode. Python’s dynamic typing reduces boilerplate, unlike C++’s static typing, making it ideal for CI/CD and AI/ML tasks, though less performant for compute-heavy applications.

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 tasks in CI/CD or web apps, the GIL is less impactful, but multiprocessing is preferred for parallel processing.

5. How do Python lists and tuples differ in functionality?

Lists are mutable, allowing modifications like appending elements, ideal for dynamic data engineering tasks, while tuples are immutable, ensuring data integrity for fixed datasets. Lists consume more memory, supporting methods like pop(), whereas tuples are lightweight with faster iteration. Lists suit CI/CD pipeline data, while tuples are used for configurations.

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

Python dictionaries are mutable key-value stores enabling fast O(1) lookups for data engineering tasks like schema mapping or caching API responses in CI/CD pipelines. They’re ideal for storing configurations or metadata, offering flexibility in web development or analytics workflows.

7. What is a Python set, and what are its primary 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 user IDs in analytics. Sets provide efficient membership testing, making them valuable for large-scale data processing.

8. How does Python manage memory for engineering tasks?

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

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

Dynamic typing allows variables to change types without explicit declarations, enhancing flexibility in rapid prototyping for CI/CD or data engineering scripts. It simplifies code but requires careful error handling to avoid runtime issues. This feature accelerates development in agile environments.

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

Generators yield values incrementally, reducing memory usage for large datasets in data engineering. Using yield, they enable lazy evaluation, ideal for streaming CI/CD logs or processing big data with Pandas. Generators optimize performance in memory-constrained environments.

11. How does Python support list comprehensions?

List comprehensions provide concise syntax for creating lists, like [x*2 for x in range(10)], outperforming loops in data engineering tasks. They enhance readability and efficiency, making them ideal for transforming datasets in CI/CD pipelines or analytics.

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

Modules are reusable Python files containing functions, classes, or variables, imported using import. They organize code for CI/CD pipelines, enabling modularity in data engineering or web projects. Standard libraries like os or custom modules enhance scalability.

13. How does Python’s import system work?

Python’s import system loads modules into the namespace, allowing access to functions or classes. Relative and absolute imports organize CI/CD or data engineering codebases, while sys.path determines module search paths. Understanding imports ensures modular, scalable applications.

14. What is the difference between str and repr in Python?

str returns a human-readable string representation of an object, used in print(), while repr provides a detailed, developer-friendly representation for debugging. In CI/CD pipelines, str aids logging, while repr supports diagnostics.

15. How do you implement inheritance in Python?

Inheritance allows classes to inherit attributes and methods, enabling code reuse in web or data engineering. Single, multiple, or multilevel inheritance supports flexible designs, like extending Django models. Understanding inheritance demonstrates OOP proficiency.

Python Programming and Best Practices

16. How do you optimize Python code for performance?

Optimizing Python code enhances efficiency in engineering workflows.

• Built-in Functions: Use map() or filter() for faster iterations.
• List Comprehensions: Replace loops for concise data processing.
• Profiling: Leverage cProfile to identify CI/CD bottlenecks.
• Libraries: Use NumPy for vectorized operations in data engineering.
• Caching: Implement memoization for repetitive tasks.
  These strategies ensure scalable code.

17. What are Python decorators, and how are they applied?

Decorators are functions that modify other functions’ behavior, used for logging, authentication, or timing in CI/CD pipelines. They wrap functions to add functionality, like logging API calls in Flask apps. Mastery of decorators showcases advanced Python skills.

18. How do you handle exceptions in Python applications?

Python’s try-except blocks catch errors like ValueError or KeyError, ensuring robust CI/CD pipelines. Specific exception handling prevents crashes, while finally ensures resource cleanup, like closing database connections. Proper exception management is critical for reliable Python applications.

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

The == operator 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 distinction is crucial for debugging data engineering pipelines.

20. How do you implement context managers in Python?

Context managers, using with, manage resources like files or database connections in CI/CD scripts. The enter and exit methods ensure setup and cleanup, preventing leaks. For example, with open('file.txt') as f: ensures file closure, a best practice for robust Python applications.

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

*args accepts variable positional arguments, and **kwargs handles variable keyword arguments, enhancing flexibility in CI/CD or data engineering scripts. For example, *args collects inputs for logging, and **kwargs passes configuration dictionaries. Their use demonstrates Python’s versatility.

22. How does Python support object-oriented programming (OOP)?

Python supports OOP through classes, inheritance, polymorphism, and encapsulation. Classes define data models for Django apps, inheritance enables code reuse, polymorphism supports flexible methods, and encapsulation protects data. OOP is critical for scalable web and data engineering projects.

23. 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 tasks in CI/CD scripts or data engineering but less readable for complex logic.

24. How do you manage dependencies in Python projects?

Dependency management uses pip and virtualenv to isolate environments, with requirements.txt listing dependencies for CI/CD pipelines. Tools like poetry or pipenv manage complex setups, ensuring reproducible builds in cloud-based Python applications.

25. What are Python’s built-in data types, and how are they used?

Python’s built-in data types include lists, tuples, dictionaries, sets, strings, integers, and floats. Lists and dictionaries manage dynamic CI/CD data, tuples ensure immutable configurations, and sets handle deduplication. Understanding their use optimizes data engineering tasks.

26. How does Python’s zip function work?

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 or in CI/CD for processing parallel inputs, enhancing efficiency.

27. What is the difference between deepcopy and shallowcopy?

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

28. How do you implement iterators in Python?

Iterators use iter and next methods to enable custom iteration, like processing CI/CD logs. The itertools module provides utilities like cycle or chain for efficient iteration in data engineering tasks.

29. What is Python’s collections module?

The collections module offers specialized data structures like namedtuple, deque, and Counter. Counter counts occurrences in data engineering, deque optimizes queue operations for CI/CD tasks, and namedtuple enhances readability.

30. How do you handle file operations in Python?

File operations use open() with modes like r or w, and with statements ensure closure, like with open('file.txt', 'r') as f:. This is critical for CI/CD log processing or data engineering, preventing resource leaks.

Python Frameworks and Web Development

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

Django is a high-level Python framework for rapid, secure web development, following the MVC pattern. It offers ORM, authentication, and admin interfaces, simplifying database interactions and API development for CI/CD-integrated web applications.

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

Flask is a lightweight, flexible framework for small-scale or microservices-based APIs, while Django’s batteries-included approach suits complex web applications. Flask offers customization for CI/CD-driven APIs, whereas Django’s ORM streamlines large-scale development.

33. How do you secure a Django application?

Securing Django involves multiple strategies for robust web development.

• CSRF Protection: Enable tokens to prevent cross-site attacks.
• Authentication: Use Django’s 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-integrated apps.

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

Django’s ORM abstracts database operations, allowing Python objects to interact with databases like PostgreSQL without SQL. It supports migrations and simplifies data modeling for CI/CD pipelines, reducing complexity in web development.

35. How is Flask used for building REST APIs?

Flask creates lightweight REST APIs with minimal setup, using routes to handle HTTP methods like GET or POST. Extensions like Flask-RESTful enhance CI/CD-driven API development, supporting scalable microservices architectures.

36. What are Django migrations, and why are they critical?

Django migrations manage database schema changes, generating SQL from model definitions. They ensure consistent database states across CI/CD environments, enabling seamless updates in production web applications.

37. How do you handle static files in Django?

Django serves static files (CSS, JavaScript) using STATICFILES_DIRS and the collectstatic command for production. In CI/CD pipelines, files are stored in AWS S3 or CloudFront for scalability, ensuring efficient web application performance.

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

Web Server Gateway Interface (WSGI) connects Python web applications to servers. Tools like Gunicorn implement WSGI, enabling Django or Flask apps to handle HTTP requests in CI/CD deployments. WSGI ensures scalability.

39. How do you optimize Django application performance?

Django performance optimization enhances web application efficiency.

• Query Optimization: Use select_related to minimize database 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-driven apps.

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

Flask Blueprints modularize large applications by organizing routes and views, improving code maintainability. They enable separation of concerns in CI/CD-driven APIs, supporting scalable microservices development.

41. How do you handle authentication in Flask?

Flask uses extensions like Flask-Login or JWT for authentication in CI/CD-driven APIs. Token-based authentication secures endpoints, integrating with AWS Secrets Manager for credential storage, ensuring robust and scalable web applications.

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

Django middleware processes requests and responses globally, enabling logging, authentication, or rate-limiting in web applications. Custom middleware in CI/CD pipelines adds functionality like request tracking, ensuring scalability and security.

43. 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 and deployment, ensuring scalable web services.

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

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

45. How do you manage database connections in Django?

Django manages database connections via its ORM, pooling connections for efficiency. Settings like CONN_MAX_AGE optimize performance in CI/CD pipelines, ensuring scalable web applications with minimal overhead.

Data Engineering and Python

46. How is Python used in data engineering pipelines?

Python powers data engineering with libraries like 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 data transformation and analytics.

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

Pandas provides DataFrames for data manipulation, supporting cleaning, transforming, and analyzing datasets in CI/CD pipelines. It integrates with AWS S3 for storage or Redshift for analytics, offering efficiency for data engineering tasks.

48. How does PySpark enhance big data processing?

PySpark, Apache Spark’s Python API, processes large-scale datasets in distributed environments. It supports ETL, machine learning, and analytics, integrating with AWS EMR for CI/CD pipelines, enabling scalable data engineering.

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

Apache Airflow, a Python-based tool, orchestrates data pipelines by scheduling DAGs. It automates ETL processes in CI/CD environments, integrating with AWS services like S3, ensuring scalable workflows.

50. How do you optimize Pandas for large datasets?

Optimizing Pandas enhances data engineering efficiency.

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

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

NumPy provides efficient array operations and mathematical functions, outperforming Python loops for numerical computations in CI/CD pipelines or AI/ML preprocessing. Its vectorized operations enhance performance, making it essential for data engineering.

52. How do you handle missing data in Pandas?

Pandas handles missing data with dropna() to remove nulls or fillna() to impute values like means or medians. These ensure data integrity in CI/CD-driven analytics pipelines, enabling robust data engineering workflows.

53. 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 pipelines for analytics or AI/ML, ensuring scalable data processing.

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

Large CSV files are processed using Pandas chunking to read data in batches, or Dask for out-of-memory datasets. These integrate with CI/CD pipelines for scalable data engineering, ensuring efficient processing.

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

SQLAlchemy is a Python ORM abstracting SQL queries into Python objects, simplifying database interactions in CI/CD pipelines. It integrates with PostgreSQL or AWS RDS, supporting scalable data engineering.

56. 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. These prevent errors in analytics or ML workflows.

57. 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 computing, supporting big data analytics or ML preprocessing.

58. How do you integrate Python with Apache Kafka?

Python integrates with Kafka using confluent-kafka or kafka-python to stream data in CI/CD pipelines. Producers send data, and consumers process it for real-time analytics, ensuring scalable workflows.

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

Python automates ETL with Pandas for transformation, SQLAlchemy for database extraction, and AWS Glue for loading data into S3 or Redshift. It streamlines CI/CD-driven data pipelines, ensuring scalability and efficiency.

60. How do you visualize data in Python?

Data visualization uses Matplotlib for plots, Seaborn for statistical graphics, or Plotly for interactive visuals. These integrate with CI/CD pipelines to generate analytics reports, enhancing insights for stakeholders.

AI/ML and Python Development

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

Python excels in AI/ML with libraries like TensorFlow, PyTorch, and Scikit-learn for model building and training. It integrates with AWS SageMaker for CI/CD-driven ML pipelines, enabling scalable deployment for predictive analytics.

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

TensorFlow is an open-source Python library for machine learning, supporting neural networks and deep learning. It enables CI/CD-integrated model training and deployment with AWS SageMaker, ensuring scalability for AI/ML applications.

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

Scikit-learn provides tools for classification, regression, and clustering, simplifying data preprocessing and model evaluation in CI/CD pipelines. It integrates with AWS for scalable ML deployments, making it accessible for engineers.

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

PyTorch emphasizes flexibility with dynamic computation graphs, ideal for AI/ML research, while TensorFlow focuses on production-ready scalability for CI/CD pipelines. PyTorch’s ease of use contrasts with TensorFlow’s robustness.

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

Data preprocessing uses Pandas for cleaning, NumPy for numerical operations, and Scikit-learn for scaling or encoding. These ensure data quality in CI/CD-driven ML pipelines, integrating with AWS SageMaker for model training.

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

AWS SageMaker builds, trains, and deploys ML models using Python scripts for preprocessing and model development. It integrates with CI/CD pipelines via CodePipeline, automating scalable ML workflows.

67. How do you evaluate ML models in Python?

Model evaluation uses Scikit-learn metrics like accuracy, precision, or RMSE, with cross-validation for robustness. Confusion matrices visualize performance in CI/CD pipelines, ensuring reliable ML models.

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

Overfitting occurs when models perform well on training data but poorly on unseen data. Prevention includes regularization (Lasso, Ridge), cross-validation, and dropout in TensorFlow, ensuring robust CI/CD-driven models.

69. How do you deploy ML models using Python?

ML models are deployed using Flask or FastAPI for APIs, integrating with AWS Lambda or SageMaker for CI/CD automation. Docker ensures scalability, and CloudWatch monitors performance.

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

Keras is a high-level API for building neural networks, often used with TensorFlow. It simplifies model creation for CI/CD-driven AI/ML pipelines, integrating with AWS SageMaker for scalable deployment.

71. How do you handle imbalanced datasets in Python?

Imbalanced datasets are managed using Scikit-learn’s oversampling (SMOTE), undersampling, or class weighting. These ensure robust ML models in CI/CD pipelines, addressing skewed data in analytics.

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

XGBoost is a gradient boosting library for machine learning, offering high performance for classification and regression. It integrates with CI/CD pipelines for scalable ML deployment.

73. How do you implement feature engineering in Python?

Feature engineering uses Pandas for creating new features, Scikit-learn for encoding, and NumPy for transformations. It enhances ML model performance in CI/CD pipelines, ensuring accurate predictions.

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

NLTK is a Python library for natural language processing, supporting tasks like tokenization or sentiment analysis. It integrates with CI/CD pipelines for text processing in AI-driven applications.

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

Optimizing ML training uses efficient algorithms (XGBoost), hyperparameter tuning with GridSearchCV, and distributed training with AWS SageMaker. These ensure scalable CI/CD-driven models, reducing training time.

Cloud and CI/CD Integration with Python

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

Python integrates with AWS CodePipeline, CodeBuild, and Lambda to automate CI/CD workflows. Boto3 scripts manage resources like S3 or EC2, ensuring scalable cloud-native pipelines for data engineering or web apps.

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

Boto3, the AWS SDK for Python, enables programmatic access to services like S3, EC2, or Lambda. It automates CI/CD tasks, such as uploading artifacts or scaling resources, ensuring seamless cloud integration.

78. How do you automate AWS tasks with Python?

Python automates AWS tasks using Boto3 to manage S3 buckets, EC2 instances, or Lambda functions in CI/CD pipelines. Scripts deploy applications, monitor CloudWatch metrics, or configure IAM, ensuring scalable workflows.

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

AWS Lambda runs Python functions for CI/CD events, like S3 uploads or API triggers. Python’s lightweight syntax and Boto3 integration enable cost-efficient automation, reducing infrastructure overhead.

80. How do you secure Python applications on AWS?

Securing Python apps on AWS 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.

81. How do you monitor Python applications on AWS?

Monitoring uses CloudWatch for Lambda or EC2 metrics, X-Ray for CI/CD request tracing, and CloudTrail for API audits. Python scripts parse logs, ensuring observability and rapid issue resolution.

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

AWS CodePipeline automates CI/CD workflows, and Python scripts in CodeBuild or Lambda handle build and deployment tasks. Boto3 triggers pipeline stages, integrating with S3 for artifacts.

83. How do you use Python with AWS S3?

Python uses Boto3 to interact with S3, uploading CI/CD artifacts, logs, or ML data. Scripts manage bucket policies or retrieve files, integrating with pipelines for automated workflows.

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

AWS Glue is a serverless ETL service using Python scripts to transform data in S3 or Redshift. It automates CI/CD-driven analytics or ML pipelines, ensuring scalable data processing.

85. How do you deploy Python applications on AWS?

Python apps deploy using Elastic Beanstalk for simplicity, ECS for containers, or Lambda for serverless. CI/CD pipelines with CodePipeline automate deployments, integrating with S3 and CloudWatch.

Testing and Debugging in Python

86. How do you write unit tests in Python?

Unit tests use unittest or pytest to verify Python code in CI/CD pipelines. Tests isolate functions, using unittest.mock to mock dependencies, ensuring robust data engineering or web apps.

87. What is pytest, and why is it preferred for testing?

Pytest is a Python testing framework with simple syntax, supporting fixtures and parameterization. It simplifies test discovery and reporting in CI/CD pipelines, making it preferred over unittest for scalability.

88. How do you debug Python code?

Debugging uses pdb for interactive debugging, logging for tracing, or PyCharm for breakpoints. In CI/CD pipelines, CloudWatch logs or print statements identify issues, ensuring robust applications.

89. What is mocking in Python testing?

Mocking with unittest.mock simulates dependencies like APIs or databases in CI/CD tests. It isolates code, ensuring tests focus on logic without external calls, improving reliability.

90. How do you test Python APIs?

Testing APIs uses pytest with requests to verify HTTP responses in CI/CD pipelines. Tools like Postman validate endpoints, while mocking simulates external services, ensuring robust APIs.

91. How do you ensure code coverage in Python?

Code coverage uses pytest-cov to measure tested code in CI/CD pipelines. High coverage ensures robust applications, with tools like Coveralls integrating with AWS CodeBuild for reporting.

92. What is doctest, and when is it used in Python?

Doctest embeds tests in docstrings, verifying code examples in CI/CD pipelines. It’s used for simple tests or documentation-driven testing, ensuring code and docs align.

Advanced Python Concepts

93. What are metaclasses in Python, and when are they used?

Metaclasses define class behavior, used in frameworks like Django’s ORM for model customization. They enable advanced functionality in CI/CD-driven apps, demonstrating deep Python knowledge.

94. How does Python support functional programming?

Python supports functional programming with lambda functions, map(), filter(), and list comprehensions. These enable stateless operations for data engineering or CI/CD tasks, improving code efficiency.

95. How do you implement multithreading in Python?

Multithreading uses the threading module for I/O-bound tasks like API calls in CI/CD pipelines. The GIL limits CPU-bound performance, so multiprocessing is preferred for parallel tasks.

96. What is asyncio, and how is it used in Python?

The asyncio library enables asynchronous programming for concurrent I/O operations, like API requests in CI/CD pipelines. Using async and await, it improves performance for web or data tasks.

97. 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.

98. 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.

Python in Production and DevOps

99. How do you containerize Python applications?

Containerizing Python apps uses Docker to package code and dependencies, ensuring consistent CI/CD deployments. Dockerfiles define environments, integrating with AWS ECS or EKS for scalability.

100. What is GitOps, and how does Python support it?

GitOps uses Git for declarative CI/CD management, and Python scripts automate AWS resource provisioning via Boto3 or CloudFormation. This ensures version-controlled, scalable pipelines.

101. How do you monitor Python applications in production?

Monitoring uses logging, Prometheus for metrics, and AWS CloudWatch for CI/CD insights. Python’s logging module tracks errors, integrating with CloudWatch for observability.

102. How do you prepare for Python Engineer interviews?

Preparation involves hands-on practice and structured study.

• Coding Practice: Solve LeetCode problems for algorithms.
• Projects: Build CI/CD pipelines with Flask or Pandas, using AWS.
• 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.