Top 45 Infosys Python Interview Questions and Answers

Infosys is one of India’s largest IT services companies, known for its global presence and innovation in technology consulting. With a strong focus on digital transformation and enterprise solutions, Infosys regularly recruits skilled developers proficient in Python, one of the most widely used programming languages today. Whether you’re aiming for a fresher role or an advanced position, understanding the kind of Python questions Infosys asks can give you a strong edge. In this blog, we will cover the top Infosys Python interview questions and answers across experience levels.

Infosys Interview Process

Infosys follows a well-defined recruitment process to assess candidates for technical roles across experience levels. Whether you’re applying as a fresher or an experienced professional, the selection process is designed to evaluate technical knowledge, problem-solving abilities, and communication skills. Understanding each stage of the interview helps you prepare strategically and approach the process with confidence. The process of the Infosys interview is as follows:

1. Online Assessment: This is the initial shortlisting round conducted online. It typically includes sections on quantitative aptitude, logical reasoning, verbal ability, and coding. The difficulty may vary depending on your experience level, but the goal is to assess your problem-solving ability and technical foundation. Candidates must perform well in this round to proceed further.
2. Technical Interview: In this round, interviewers assess your understanding of programming concepts, data structures, algorithms, databases, and operating systems. You may be asked to solve coding problems, explain logic, and work through technical scenarios. Candidates with hands-on experience in projects or internships should be ready to discuss them in detail.
3. Managerial Interview: This round evaluates how you approach real-world challenges. You may be asked to solve case studies, handle hypothetical project situations, or explain past work under deadlines or resource constraints. The interviewer checks your problem-solving approach, decision-making ability, and whether you’re a good fit for the team dynamics.
4. HR Interview: The final round focuses on personality, attitude, communication, and alignment with Infosys values. Questions typically revolve around your career goals, reasons for applying to Infosys, willingness to relocate, and expectations regarding salary or growth. A clear and honest approach in this round can help secure the offer.

Pro Tip: Even if you ace the technical sections, HR questions can significantly impact your success during the interview. To help you prepare with confidence, check out common Infosys HR interview questions and answers

Infosys Python Interview Questions and Answers for Freshers

Infosys hires freshers through campus placements and off-campus drives, offering packages between ₹3.5 LPA and ₹5 LPA for entry-level roles like system engineer or operations executive. Fresh graduates applying for Python roles at Infosys can expect questions focused on programming fundamentals, syntax, and basic problem-solving. Here are a few Infosys Python interview questions and answers for freshers at Infosys:

Q1. What is Python, and what are its key features?

Sample Answer: Python is a versatile and powerful programming language known for its clear syntax and ease of use. It supports multiple programming paradigms such as procedural, object-oriented, and functional programming. It is an interpreted, object-oriented, high-level programming language with dynamic semantics, which makes it ideal for rapid application development. Some of its key features include automatic memory management, a vast standard library, dynamic typing, and strong community support. It’s widely used in web development, data science, automation, and artificial intelligence.

Q2. Differentiate between a list and a tuple in Python.

Sample Answer: In Python, both lists and tuples are used to store collections of data. However, they differ in several important ways. Lists and tuples both store collections of data, but differ in mutability, performance, and memory usage. Lists are mutable, allowing changes to their contents, while tuples are immutable and cannot be altered once defined. Tuples tend to use less memory and offer faster performance due to their immutability. Lists are more flexible, whereas tuples are more secure and lightweight. 

Pro Tip: Knowing when to use a list or a tuple can significantly impact your code’s performance and reliability. For a better understanding of both, check out the differences between a list and a tuple in Python.

Q3. Explain the concept of Python’s dynamic typing.

Sample Answer: Python supports dynamic typing, which means that you don’t need to declare the type of a variable when you create it. The type of the variable is determined only at runtime. This allows for greater flexibility and faster development, but it also requires careful handling to avoid type-related errors during execution.

Q4. What are Python’s built-in data types?

Sample Answer: Python offers a wide variety of built-in data types to support different kinds of data manipulation. These include:

• Numeric types: float, int, complex
• Boolean type: bool
• Mapping type: dict
• Sequence types: list, range, tuple, str
• Binary types: bytearray, bytes, memoryview
• Set types: frozenset, set

These data types provide the foundation for handling data efficiently in Python programs.

Q5. How does Python handle memory management?

Sample Answer: Python simplifies memory management by handling allocation and deallocation automatically. The Python memory allocation and deallocation methods are automatic, as the Python developers created a garbage collector. This garbage collector identifies and removes unused or inaccessible objects to free up memory, allowing developers to focus more on application logic than low-level memory handling.

Q6. What is the difference between ‘is’ and ‘==’ operators in Python?

Sample Answer: The == and is operators are often confused but serve different purposes in Python. The ‘==’ is referred to as the equality operator, while the ‘is’ is termed as the identity operator. The == operator checks if two variables have the same value, whereas the is operator checks if two variables point to the exact same object in memory. For example, two lists with the same content might be equal (==) but not identical (is).

Q7. How do you manage packages in Python?

Sample Answer: Package management in Python is typically handled using tools like pip, which stands for ‘Python Installer Package.’ With pip, you can install, upgrade, and remove packages from the Python Package Index (PyPI). You can also use virtual environments via venv or virtualenv to manage dependencies and prevent conflicts between projects.

Q8. What are Python’s common file handling modes?

Sample Answer: Python provides several file-handling modes for working with files. These include:

• r – Read mode (default)
• w – Write mode (overwrites existing file)
• a – Append mode
• b – Binary mode
• t – Text mode (default)
• x – Exclusive creation, fails if file exists

You can also read from a file, write to a file, and delete files, and it’s important to always close the file using close() or a with block to free up resources.

Q9. Explain the use of decorators in Python.

Sample Answer: Decorators in Python are a powerful tool that allows developers to modify or extend the behavior of functions or methods without changing their actual code. It enables users to extend an existing object’s functionality without altering its original structure. Decorators are commonly used for logging, access control, memoization, and instrumentation.

Q10. What is the purpose of the ‘self’ keyword in Python classes?

Sample Answer: In Python, the self keyword is used in instance methods to refer to the object itself. It is used to represent an instance of the class. This allows access to instance variables and methods within the class. It helps differentiate between instance attributes and local variables.

Q11. How does exception handling work in Python?

Sample Answer: Python uses try, except, else, and finally blocks for exception handling. Python’s exception handling system manages unexpected issues that arise while a program is running, preventing crashes and allowing for controlled error recovery. When an error occurs in the try block, the code inside the except block is executed. This prevents programs from crashing and provides a way to handle unexpected situations gracefully.

Q12. What are list comprehensions, and how are they used?

Sample Answer: List comprehensions provide a concise way to create lists in Python. List comprehension offers a shorter syntax when you want to create a new list based on the values of an existing list. For example:

squares = [x*x for x in range(10)]

This is both more readable and efficient than using a traditional for loop.

Q13. How do you approach debugging and profiling in Python applications?

Sample Answer: I use built-in tools like pdb for step-by-step debugging and logging for real-time insight. For performance profiling, I use cProfile, line_profiler, and memory_profiler to identify performance bottlenecks and memory issues. In production, I integrate tools like Sentry and New Relic to monitor errors and performance metrics continuously.

Q14. What is the purpose of the ‘with’ statement in Python?

Sample Answer: The with statement in Python is used for resource management, such as file handling, where setup and cleanup actions are needed. It is used to simplify the management of resources that need to be acquired and released in a specific order. When using with, the resource is automatically cleaned up once the block of code is executed, even if an error occurs.

Pro Tip: If you’re still in college and aiming to step into Infosys, learn how to get an internship at Infosys with real tips and a detailed recruitment process.

Infosys Python Interview Questions for Intermediate Level

Candidates with 2–4 years of experience applying for Python-based roles at Infosys are usually assessed on applied coding, OOPs, and real-world implementation scenarios. Roles like senior systems engineer or technology analyst fall in this bracket, with average salaries ranging from ₹6 LPA to ₹10 LPA, depending on skills and project exposure. Here are a few Infosys Python interview questions and answers for intermediate-level candidates:

Q15. Explain the difference between shallow copy and deep copy in Python.

Sample Answer: In Python, a shallow copy creates a new object, but it does not create copies of nested objects within it—it just references them. In Shallow copy, a copy of the original object is stored, and only the reference address is finally copied.
On the other hand, a deep copy creates a new object and recursively copies all objects found within the original, resulting in a fully independent clone. In Deep copy, the copy of the original object and the repetitive copies are both stored.

Q16. How does Python manage memory, and what is the role of the garbage collector?

Sample Answer: Python uses an automatic memory management system. It automatically cleans up any unused objects based on reference counting and object allocation and deallocation.

The built-in garbage collector keeps track of all objects and frees up memory by removing those that are no longer in use. This is particularly important in breaking reference cycles that regular reference counting can’t handle.

Q17. What are Python generators, and how do they differ from iterators?

Sample Answer: Generators are a simpler way of creating iterators using functions and the yield keyword. Unlike regular iterators that require class definitions with __iter__() and __next__() methods, generators automatically handle iteration state.
They are more memory-efficient as they yield one item at a time and don’t store the entire sequence in memory. You can iterate through them using loops just like any iterator.

Q18. How would you secure a Python-based REST API against common vulnerabilities?

Sample Answer: To secure a Python-based REST API, the key measures I’d take include:

• Authentication: OAuth2/JWT with libraries like Authlib or PyJWT.
• Input validation: Use Pydantic or Django validators to sanitize payloads.
• Rate limiting: Implement via django-ratelimit or API gateways (e.g., Kong).
• SQL injection: Avoid raw queries; use ORMs (SQLAlchemy, Django ORM).
• CORS: Restrict origins with django-cors-headers or FastAPI middleware.

Q19. How do you handle exceptions in Python, and what is the difference between ‘try-except’ and ‘try-finally’ blocks?

Sample Answer: Python uses try, except, else, and finally blocks to handle exceptions gracefully.

The try block lets you test a block of code for errors. The except block lets you handle the error. The else block lets you execute code when there is no error. The finally block lets you execute code, regardless of the result of the try- and except blocks.

While try-except is used to catch and manage exceptions, try-finally ensures that cleanup code is always executed, whether or not an error occurs.

Q20. What is the purpose of the ‘with’ statement in Python?

Sample Answer: The with statement is used to simplify resource management in Python. It simplifies the management of resources that need to be acquired and released in a specific order.

For example, when working with files, it ensures the file is automatically closed after its block of code is executed, reducing the risk of memory leaks or resource locking.

Q21. How do you manage dependencies and virtual environments in Python?

Sample Answer: To manage dependencies effectively, it’s best to isolate them per project. You can use Python’s venv module to manage dependencies independently for each project.
This ensures packages for one project don’t interfere with those of another. Tools like pip are used to install and manage packages inside these virtual environments.

Q23. What are Python decorators, and how are they used?

Sample Answer: Decorators in Python are powerful tools for modifying the behavior of functions or methods without changing their code. A decorator is essentially a function that takes another function as an argument and returns a new function with enhanced functionality.
They’re often used for tasks like logging, authentication, or timing function execution, and are applied using the @decorator_name syntax.

Q24. How do you implement multithreading in Python, and what are its limitations?

Sample Answer: Multithreading in Python is implemented using the threading module by creating Thread objects that run target functions concurrently. The limitations include:

• GIL (Global Interpreter Lock): Prevents true parallelism for CPU-bound tasks.
• Best for I/O-bound operations.
• Shared memory can lead to race conditions.
• Not ideal for heavy computation, so you must use multiprocessing instead.

Q25. How do you optimize performance in Python applications?

Sample Answer: To optimize Python performance, you can use techniques like:

• Using built-in functions and libraries (which are optimized in C)
• Avoiding unnecessary loops and using list comprehensions
• Profiling code with modules like cProfile
• Using Python libraries like NumPy for numerical operations
  You can also improve performance by implementing concurrency using asyncio, multiprocessing for CPU-bound tasks, and caching with tools like functools.lru_cache.

Q27. Explain the use of lambda functions in Python.

Sample Answer: Lambda functions are powerful, concise tools for creating small, anonymous functions on the fly.

They’re commonly used in places where functions are required temporarily, like in map() function, filter(), or sorted().

Example:

add = lambda x, y: x + yprint(add(3, 5))  # Output: 8

They offer a neat way to write simple functions inline.

Q28. What are Python’s built-in data structures, and when would you use each?

Sample Answer: Python provides several built-in data structures that fall under two categories:
Mutable types (lists, dictionaries, and sets) – useful when you need to modify the data.
Immutable types (tuples) – suitable when data must remain constant.

• Lists: Ordered collections for general-purpose use
• Dictionaries: Key-value pairs for fast lookups
• Sets: Unordered, unique elements for membership testing
• Tuples: Fixed, ordered collections when immutability is needed

Q29. How do you connect a Python application to a database, and what libraries are commonly used? 

Sample Answer: To connect Python applications to databases, you typically use libraries like:

• sqlite3 for local databases
• psycopg2 for PostgreSQL
• mysql-connector-python or PyMySQL for MySQL

ORMs like SQLAlchemy and Django ORM are also commonly used to abstract raw SQL queries and manage database models in an object-oriented way.

Pro Tip: Want to boost your chances of getting hired at Infosys? Check out how to get a job at Infosys to learn how to apply and get tips to stand out during the interviews.

Infosys Python Interview Questions and Answers for Experienced Professionals

Professionals with over 5 years of experience are interviewed for advanced Python roles such as technical architect or specialist programmer. The questions often cover system design, performance optimization, and architecture-level thinking. Infosys offers compensation between ₹12 LPA to ₹25 LPA or more, depending on expertise and leadership responsibilities. Here are a few Infosys Python interview questions and answers for experienced candidates:

Q30. How would you design a scalable microservices architecture using Python?

Sample Answer: To design a scalable microservices architecture in Python, I follow a structured approach.

• Step 1: Setting Up the Foundation – I begin by isolating each service in its own codebase, containerizing it with Docker, and using frameworks like Flask or FastAPI for lightweight service creation.
• Step 2: Designing RESTful Endpoints – Each microservice exposes clearly defined RESTful APIs, ensuring loose coupling.
• Step 3: Implementing Communication Patterns – Services communicate asynchronously using message brokers like RabbitMQ or Kafka.
• Step 4: Error Handling and Monitoring – I integrate tools like Sentry and Prometheus for monitoring, logging, and alerting.
• Step 5: Security and Authentication – Each service is protected via API gateways and OAuth2-based mechanisms.
• Step 6: JWT Authentication Implementation – I secure APIs using JWT-based authentication for stateless session handling.
• Step 7: API Security Installation – I ensure rate limiting, input validation, and role-based access control are in place for all services.
  This layered approach ensures scalability, maintainability, and security of the overall system.

Q31. Explain the role of metaclasses in Python and provide a use case.

Sample Answer: In Python, a metaclass is a class of a class that defines how classes behave. You can think of it as the blueprint for classes, just like classes are blueprints for objects. A metaclass allows for other classes to be instantiated as objects of the metaclass. One practical use case is enforcing coding standards or automatically registering classes. For instance, you could use a metaclass to ensure all classes in a plugin system have a particular method implemented.

Q32. How do you handle concurrency and parallelism in Python applications?

Sample Answer: To handle concurrency and parallelism, I choose between threading, multiprocessing, and asyncio based on the task type. 

• Threading is one of the most well-known approaches to attaining parallelism and concurrency in Python, especially effective for I/O-bound operations. 
• For CPU-bound tasks, I prefer multiprocessing to bypass the limitations of the GIL. 
• In async applications, I use asyncio for lightweight coroutines.

Q33. What strategies do you employ for memory optimization in large-scale Python applications?

Sample Answer: To optimize memory, I monitor object creation using tools like objgraph, limit large data structures’ lifetime, and avoid memory leaks via weak references. I use specialized libraries such as NumPy and pandas for handling large datasets efficiently due to their low-level memory management and vectorized operations.  Additionally, I use generators, __slots__, and batching techniques to reduce the memory footprint.

Q34. Describe the process of implementing a custom iterator in Python.

Sample Answer: To implement a custom iterator in Python, you need to understand the iterator protocol. This involves defining two essential methods: __iter__() and __next__(). 

• __iter__() returns the iterator object itself, while 
• __next__() should return the next value in the sequence. When there are no more items to return, __next__() must raise a StopIteration exception.

This is especially useful when creating custom iterable objects such as paginated APIs or custom data loaders.        

Q35. How do you manage package dependencies and virtual environments in complex projects?

Sample Answer: For complex projects, I rely on venv or virtualenv to isolate environments and ensure reproducibility. I create a requirements.txt file that lists all the packages and their versions required by the project, which allows consistent setup across environments. For more robust dependency management, I sometimes use pipenv or Poetry, especially when working in teams. 

Q36. Explain the use of context managers and the ‘with’ statement in resource management.

Sample Answer: Context managers in Python are used to manage resources like file streams, network connections, or database sessions. 

The with statement automatically calls the __enter__() method of the context manager before entering the block and the __exit__() method when exiting, ensuring that resources are cleaned up properly. This prevents resource leaks and ensures safer code execution.        

Q37. How do you implement logging in Python applications for effective debugging and monitoring?

Sample Answer: I use Python’s built-in logging module to create logs at various levels (DEBUG, INFO, WARNING, ERROR, CRITICAL). For larger applications, I configure log handlers to write logs to different destinations like files, streams, or external systems like ELK or Splunk. I ensure logs include timestamps, module names, and correlation IDs for easier traceability during debugging and monitoring.

Q38. Provide examples of deep copy and shallow copy.

Sample Answer: A shallow copy generates a new object, but instead of duplicating any nested elements, it simply replicates their references.

Example:

import copy original = [[1, 2], [3, 4]] shallow = copy.copy(original)

Changes in nested lists will reflect in both.

A deep copy duplicates all nested elements recursively:

deep = copy.deepcopy(original)

Now both outer and inner objects are independent.

Q39. How do you ensure thread safety in multi-threaded Python applications?

Sample Answer: To ensure thread safety, I use thread locks (threading.Lock()), semaphores, or condition variables to manage access to shared resources. I also minimize shared state between threads and prefer thread-safe queues (queue.Queue) for communication. Designing with immutability and leveraging concurrent data structures helps avoid race conditions and data corruption.

Q40. What are the best practices for exception handling in large Python codebases?

Sample Answer: Best practices include:

• Catching specific exceptions instead of using a bare except
• Logging exceptions with tracebacks
• Raising custom exceptions for clarity
• Avoiding silent failures

In large codebases, I ensure consistent exception structure using base exception classes and apply exception handling close to the source of the error to maintain code readability and traceability.

Q41. How do you integrate Python applications with databases, and what ORMs have you used?

Sample Answer: I’ve integrated Python with databases using both direct connectors and Object-Relational Mappers (ORMs).

Commonly used libraries include psycopg2 for PostgreSQL and mysql-connector-python for MySQL.

ORMs I’ve used include SQLAlchemy and Django ORM – they abstract raw SQL queries and make data modeling more intuitive, especially in large-scale applications.

Q42. Explain the use of decorators and how they can be applied in real-world scenarios.

Sample Answer: Decorators are functions that modify the behavior of other functions or methods without changing their code.

They are widely used in real-world scenarios such as logging, enforcing access control, caching results, and measuring execution time.

Example:

def logger(func):
	def wrapper(*args, **kwargs):
    	print(f"Running {func.__name__}")
    	return func(*args, **kwargs)
	return wrapper

Decorators improve code reusability and cleanliness.

Q43. How do you perform unit testing in Python, and which frameworks do you prefer?

Sample Answer: I prefer using unittest and PyTest frameworks for unit testing in Python.
unittest comes with Python and follows a class-based structure, while PyTest offers a simpler syntax and powerful fixtures system. I use mocking libraries like unittest.mock to isolate dependencies and run tests frequently through CI/CD pipelines to ensure code reliability.

Q44. Describe the process of optimizing Python code for performance.

Sample Answer: To optimize Python code, I:

• Utilize built-in functions and libraries that are implemented in C for speed
• Refine algorithms to reduce time complexity
• Optimize data structures by choosing the right ones for the task
• Implement caching or memoization using tools like functools.lru_cache

I also use profiling tools like cProfile or line_profiler to identify bottlenecks before optimizing.

Q45. How do you handle configuration management in Python applications across different environments?

Sample Answer: For environment-based configuration, I use environment-specific configuration files (e.g., .env, .ini, or .yaml) along with libraries like python-decouple or dotenv. This helps manage settings like API keys, database URLs, and debug flags without hardcoding them into the application, making deployment safer and more manageable.

Pro Tip: During the interview at Infosys, candidates should expect technical questions that test logic, code efficiency, and a solid understanding of syntax. To help you prepare, check out the top Infosys coding interview questions and answers.

Tips to Crack the Infosys Python Interview Round

Infosys is known for its rigorous selection process, especially when it comes to technical roles that involve programming in Python. The Python interview round evaluates both conceptual understanding and practical application. It typically includes questions on data structures, algorithms, object-oriented programming, and real-world coding problems.

Here are a few tips to help you succeed in the Infosys Python interview questions and answers round:

1. Strengthen Core Python Concepts: A solid understanding of Python fundamentals will help you excel in your interviews. Revisit essential topics like data types, loops, functions, and string operations. Mastering these core concepts ensures you can confidently tackle both basic and intermediate questions with ease.
2. Practice Logical and Coding Questions: Regularly solving coding problems on arrays, recursion, and sorting will sharpen your problem-solving skills. To maximize your chances, challenge yourself with more complex problems. 
3. Learn Python-Specific Features: Understand list comprehensions, lambda functions, and exception handling.. Mastering these will help you stand out and prove your proficiency in Python.
4. Focus on Object-Oriented Programming: Infosys highly values candidates with a strong grasp of OOP principles. Know how to implement classes, inheritance, and polymorphism in Python. The ability to explain and apply these concepts will help you showcase your technical expertise during the interview.
5. Improve Debugging and Code Tracing Skills: Python interviews often involve debugging and optimizing existing code. To excel in this area, regularly practice identifying and fixing bugs in sample code. Focus on improving your ability to quickly trace the flow of execution, spot issues, and apply optimal solutions.

Conclusion

We have covered a wide range of Infosys Python interview questions tailored for freshers, intermediate-level candidates, and experienced professionals. These questions reflect the practical and technical expectations Infosys has for Python developers across different roles. By preparing these topics thoroughly, you’ll be better equipped to approach the Infosys interview process with confidence and clarity. For more preparation help, check out our blog on Python coding interview questions and answers to strengthen your coding skills and tackle real-world programming challenges.

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