20+ Python Developer Lead Interview Questions and Answers

List comprehension is a concise way to create lists in Python by iterating over an existing list or iterable.

• List comprehension is more concise and readable than traditional loops.

• It can be used to filter elements, perform operations on elements, or create new lists based on existing ones.

• Example: squares = [x**2 for x in range(10)]

• Example: even_numbers = [x for x in range(10) if x % 2 == 0]

A dictionary in Python is a collection of key-value pairs. It does not accept duplicate keys.

• A dictionary is created using curly braces {}

• Keys in a dictionary must be unique, but values can be duplicated

• Example: my_dict = {'name': 'John', 'age': 30, 'city': 'New York'}

Hash mapping is a technique used to map keys to values in a data structure.

• Hash mapping involves using a hash function to generate a unique index for each key, allowing for efficient retrieval of values.

• Mutable objects can be modified after creation, while immutable objects cannot be changed.

• Bugsnag is a library used for monitoring and reporting errors in software applications.

• Agile microservices development model involves developing software in small, independent services th...read more

Main principles of OOP include encapsulation, inheritance, polymorphism, and abstraction.

• Encapsulation: Bundling data and methods that operate on the data into a single unit (class). Example: Class with private attributes and public methods.

• Inheritance: Ability to create a new class (derived class) from an existing class (base class), inheriting its attributes and methods. Example: Subclass 'Dog' inheriting from superclass 'Animal'.

• Polymorphism: Ability for objects of differe...read more

Code deployment involves creating pipelines to automate the process of moving code from development to production.

• Use version control systems like Git to manage code changes.

• Set up continuous integration/continuous deployment (CI/CD) pipelines to automate testing and deployment.

• Utilize tools like Jenkins, Travis CI, or GitLab CI to create and manage pipelines.

• Define stages in the pipeline such as build, test, deploy, and monitor for each code change.

• Implement infrastructure a...read more

Use pandas library for efficient loading and processing of large files in Python.

• Use pandas read_csv() function with chunksize parameter to load large files in chunks.

• Optimize memory usage by specifying data types for columns in read_csv() function.

• Use pandas DataFrame methods like groupby(), merge(), and apply() for efficient data processing.

• Consider using Dask library for parallel processing of large datasets.

• Use generators to process data in chunks and avoid loading entire...read more

Inter service communication in transactional systems involves designing a reliable and efficient way for services to communicate and exchange data.

• Use asynchronous messaging systems like RabbitMQ or Kafka to decouple services and ensure reliable message delivery.

• Implement RESTful APIs for synchronous communication between services, using HTTP methods like GET, POST, PUT, DELETE.

• Consider using gRPC for high-performance, low-latency communication between services, especially in...read more

Fibonacci series is a sequence of numbers where each number is the sum of the two preceding ones.

• Initialize variables for the first two numbers in the series (0 and 1)

• Use a loop to calculate the next number by adding the previous two numbers

• Continue the loop until the desired number of terms is reached

AWS Lambda is a serverless computing service provided by Amazon Web Services.

• Serverless computing service

• Allows running code without provisioning or managing servers

• Automatically scales based on incoming traffic

• Supports multiple programming languages like Python, Node.js, Java, etc.

• Pay only for the compute time consumed

Decorator is a design pattern in Python that allows adding new functionality to an existing object without modifying its structure.

• Decorators are functions that take another function as an argument and extend its behavior without modifying it directly.

• They are commonly used to add logging, timing, caching, or authentication to functions.

• Decorators use the @ symbol followed by the decorator name above the function definition.

• Example: @decorator_name def function_name(): pass

Numpy is a powerful library for numerical operations in Python, with efficient array operations and mathematical functions.

• Use vectorized operations instead of loops for better performance.

• Avoid unnecessary copying of arrays to save memory.

• Utilize broadcasting to perform operations on arrays of different shapes.

• Use numpy functions like np.sum(), np.mean(), np.max(), etc. for efficient calculations.

• Optimize code by profiling and identifying bottlenecks.

Type of inheritance in object-oriented programming determines how a subclass can inherit attributes and methods from a superclass.

• Single inheritance: a subclass inherits from only one superclass.

• Multiple inheritance: a subclass inherits from multiple superclasses.

• Multilevel inheritance: a subclass inherits from a superclass, which in turn inherits from another superclass.

• Hierarchical inheritance: multiple subclasses inherit from a single superclass.

• Hybrid inheritance: a combi...read more

Generate all valid combinations of parentheses for given n pairs

• Use backtracking to generate all valid combinations of parentheses

• Keep track of the number of open and close parentheses used

• Add open parenthesis if there are remaining open parentheses, add close parenthesis if there are more open than close parentheses

Multiple decorators in Python allow stacking functions to enhance behavior without modifying the original function.

• Decorators are functions that modify the behavior of another function.

• Multiple decorators can be applied by stacking them above a function definition.

• Example: @decorator1 @decorator2 def my_function(): pass

• The order of decorators matters; the innermost decorator is applied first.

• Use cases include logging, access control, and caching.

Optimizing code for better performance and efficiency

• Use built-in functions and libraries for faster execution

• Minimize unnecessary loops and conditions

• Avoid redundant code and optimize data structures

• Implement caching or memoization for repetitive computations

A code dictionary in Python is a collection of key-value pairs used for efficient data retrieval.

• Dictionaries are defined using curly braces: `my_dict = {}`.

• Key-value pairs are added as `my_dict['key'] = 'value'`.

• Access values using keys: `value = my_dict['key']`.

• Dictionaries are unordered and mutable, allowing dynamic changes.

• Example: `person = {'name': 'Alice', 'age': 30}`.

Optimizing Python code involves improving efficiency and performance.

• Use built-in functions and libraries instead of writing custom code

• Avoid unnecessary loops and nested loops for better performance

• Optimize data structures and algorithms for faster execution

A linked list is a data structure consisting of nodes where each node points to the next node in the sequence.

• Create a Node class with data and next pointer

• Create a LinkedList class with methods like insert, delete, search

• Example: Node class - class Node: def __init__(self, data): self.data = data self.next = None