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Deloitte Interview Questions


Most Frequently Asked Deloitte Interview Questions

1. What is the difference between abstraction and encapsulation in object-oriented programming?
2. What is the purpose of inheritance in object-oriented programming?
3. Describe the difference between a class and an object.
4. What is polymorphism in object-oriented programming systems (oops)?
5. Explain its key features.
6. What makes Python stand out compared to other programming languages?
7. What are decorators in Python?
8. What is a virtual environment in Python, and why is it used?
9. Explain the try-except block.
10. What is the lambda function in Python?

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Basic Deloitte Interview Questions for Freshers

1. What is the difference between abstraction and encapsulation in object-oriented programming?

Two primary concepts in object-oriented programming are abstraction and encapsulation. Abstraction is the process of reducing complex real-world entities down to their fundamental characteristics and presenting them in a simple manner. It points out the importance of keeping irrelevant information hidden and giving consumers access to only the necessary information.

Abstraction in programming enables you to design abstract classes or interfaces that specify the shared characteristics and operations of a collection of related objects. It offers a generalized and simplified way to represent real-world entities, concepts, or systems.

The main abstraction points are:

  • defining a common set of methods and properties by creating abstract classes or interfaces.
  • internal implementation that is kept secret.

Encapsulation is the method of merging data and methods into a single entity, referred to as a class. The methods (functions or behaviors) that operate on the data are combined with the data (attributes or properties) and are contained within a class.

Encapsulation is used to safeguard an object’s internal state and grant regulated access to it. It restricts access to and modification of data, enabling things to preserve their integrity. You can impose data validation, set access limitations, and guarantee consistency by encapsulating data and methods.

The main ideas encapsulated are:

  • creating a class from relevant data and techniques.
  • use access modifiers (such as public, private, and protected) to regulate access to internal data.

2. What is the purpose of inheritance in object-oriented programming?

In object-oriented programming, the purpose of inheritance is to facilitate code reuse, enhance modularity, and establish a hierarchical relationship between classes. Inheritance allows classes to inherit properties and behaviors from a parent class, known as the superclass or base class. By inheriting from a parent class, child classes, or subclasses, it reduces code duplication and promotes a modular approach to software development. 

Additionally, inheritance enables the extensibility and modification of existing classes without directly altering their original implementation. This promotes code organization, hierarchy, and polymorphism, as objects of different classes can be treated uniformly using a common superclass interface. Moreover, inheritance contributes to code maintenance and flexibility by allowing centralized changes in the base class to automatically propagate to the derived classes, thereby minimizing the need for individual modifications and reducing the likelihood of introducing errors.

3. Describe the difference between a class and an object.

A class and an object are two concepts that have a connection but are separate in object-oriented programming (OOP).

  • An object of a certain class will have certain properties (data) and characteristics (methods or functions), which are defined by the class. It outlines the composition and functionality of items that fall under that class. 

Example: human being

  • A class serves as a template for generating numerous instances of objects exhibiting comparable properties and behaviors. It summarizes the common characteristics and functionalities that objects of that class share.

Example: class fruit

Learn the basics of OOPs in Java through this blog!

4. What is polymorphism in object-oriented programming systems (oops)?

In simple words, we can say polymorphism is a concept of object-oriented programming systems (oops).

There are two types of polymorphism: compile-time and runtime.

  • Compile-Time Polymorphism: In object-oriented programming, there exists a feature called method overloading, which involves defining multiple methods within a class under the same name but with different parameter lists. The compiler determines which method to execute based on the arguments provided during compilation. This allows the same method name to be used for different behaviors based on the parameters used. The decision on which method to invoke is made by the compiler at compile-time rather than at runtime.
  • Runtime Polymorphism: It is also known as method overriding. In runtime polymorphism, a subclass provides the implementation of a method that is already defined in its superclass. This allows the subclass to override or modify the behavior of the superclass method while keeping the same method signature. The decision on which overridden method to execute is made at runtime based on the actual object type.

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5. What is Python? Explain its key features.

Python is a popular programming language known for its simplicity, readability, and flexibility. It was created by Guido van Rossum in 1991. It has gained recognition for its clean syntax and ease of use.

The key features of Python are the following:

  • Python focuses on code readability and dynamically determines variable types at runtime. 
  • Python is an interpreted language, which means it does not require compilation and allows for quick development and testing. 
  • It supports object-oriented programming and various programming paradigms, offering developers the freedom to choose their preferred style.
  • Python comes with an extensive standard library, providing a wide range of modules and functions for various tasks. 
  • Its cross-platform compatibility allows code to run on different operating systems without modifications. 
  • The language has a strong community of developers who contribute to its growth, and it integrates well with other languages. 

Overall, Python’s simplicity, versatility, and supportive community have made it a popular choice for a wide range of applications, from web development to data analysis and machine learning.

6. What makes Python stand out compared to other programming languages?

Python distinguishes itself from languages like Java and C++ in several aspects:

  • Syntax: Python boasts a simpler and more concise syntax than Java and C++. It uses indentation to define code blocks instead of relying on braces and semicolons, making the code easier to read and write.
  • Readability: Python places a strong emphasis on code readability and adheres to a “batteries included” approach. Its syntax is designed to be clear and straightforward, reducing the complexity typically associated with coding.
  • Dynamic Typing: Unlike Java and C++, Python adopts dynamic typing, where variable types are determined at runtime. This affords greater flexibility, as variables can be assigned different types during program execution.
  • Memory Management: Python employs automatic memory management through garbage collection, eliminating the need for manual memory allocation and deallocation tasks commonly found in C++.
  • Interpreted Nature: Python is an interpreted language, executing code line by line. This facilitates quick development and testing without the requirement of compiling code before execution, which is a characteristic of Java and C++.
  • Standard Library: Python includes an extensive standard library, offering a wide array of modules and functions for diverse tasks. This comprehensive built-in functionality reduces the reliance on external libraries and aids in saving development time.

7. What are decorators in Python?

Python decorators are a special feature that allows you to modify the behavior of functions or classes without directly changing their code. They act like wrappers around the functions or classes, adding extra functionality to them. In simple terms, a decorator is a way to enhance or extend the behavior of a function or class by applying some modifications. You can think of it as a tool that you can use to easily add extra capabilities to your functions or classes without having to modify their original code.

Let’s say you have a function that adds two numbers together. With a decorator, you can easily add some extra functionality to this function, such as logging the input and output values, without having to change the original code of the function itself. Decorators provide a convenient and reusable way to modify functions or classes in Python, making it easier to add extra features or behaviors to your code.

8. What is a virtual environment in Python, and why is it used?

A virtual environment in Python is a self-contained directory that acts as a separate and isolated Python environment. Its purpose is to manage dependencies and package installations for individual projects.

Virtual environments are utilized for two main reasons:

  • Dependency Management: Different projects often require different versions of Python packages. By creating a virtual environment for each project, you can ensure that the project has its isolated environment with specific package versions. This avoids conflicts between packages and allows you to work on multiple projects simultaneously without interference.
  • Portability: Virtual environments provide a way to share projects with others in a self-contained manner. By packaging the project along with its specific Python version and required packages within a virtual environment, you can ensure that others can reproduce the project’s environment accurately, regardless of their own Python setup.

Virtual environments help maintain clean and separate Python environments, enabling efficient dependency management, and facilitating project sharing and deployment.

9. Explain the try-except block.

When programming in Python, exceptions are used to manage errors or exceptional situations that may arise during program execution. To handle exceptions, we make use of the try-except block. The try-except block enables us to write code that could potentially trigger an exception and specify how we want to deal with that particular exception if it occurs. 

The structure of the try-except block is as follows: 

Inside the try block, we place the code that we suspect could lead to an exception being raised. If an exception does occur within the try block, the program flow immediately jumps to the corresponding except block. The except block specifies the type of exception it can handle. If the exception raised matches the specified type, the code inside the except block is executed. If the exception type does not match, the except block is skipped. The exception is passed up to the next try-except block or the outer level.

10. What is the lambda function in Python?

Lambda functions, also known as anonymous functions, are small, single-expression functions without a name. They are defined using the lambda keyword, followed by a list of parameters, a colon, and the expression to be evaluated. Lambda functions are primarily used when you need a simple function for a short period of time and don’t want to define a separate function using the def keyword. They are commonly used in combination with higher-order functions like map(), filter(), and reduce().

Intermediate Deloitte Interview Questions

11. Explain the Global Interpreter Lock (GIL).

The Global Interpreter Lock (GIL) is a distinctive mechanism specific to the CPython implementation of Python, which is widely used and considered the reference implementation. The GIL verifies that only one thread can execute Python bytecode at a time within a single Python process. The purpose of the GIL is to simplify memory management and protect critical internal data structures in Python. It guarantees the thread safety of operations like modifying object reference counts, which is important for C Python’s memory management strategy based on reference counting.

However, the GIL has implications for multi-threaded Python programs, especially those that are CPU-bound. Due to the GIL, simultaneous parallel execution of multiple CPU-bound threads is not possible, as only one thread can execute Python bytecode at any given moment. Consequently, the performance of CPU-bound tasks in CPython may not scale well with the number of threads.

12. Describe the usage of Python's 'map' and 'filter' functions.

The “map” and “filter” functions in Python are powerful built-in functions used for working with iterable objects, such as lists or tuples.

  • The “map” function takes two arguments: a function and an iterable. It applies the given function to each element of the iterable and returns an iterator that yields the results. In simpler terms, it allows you to transform each element of an iterable by applying a specific function to it. The resulting iterator can be converted to a list or used directly in a loop.
  • The “filter” function takes two arguments: a function that returns a Boolean value and an iterable. It applies the function to each element of the iterable and returns an iterator that yields only the elements for which the function returns True. In other words, it allows you to filter out elements from an iterable based on a specific condition.

13. Explain the concept of list comprehensions in Python.

List comprehensions are a concise and elegant way to create new lists in Python. They provide a compact syntax for generating lists based on existing iterables or performing transformations on elements of an iterable.


numbers = [1, 3, 5, 7, 9, 10, 12]
squared = [num**2 for num in numbers if num > 5]

In this example, the list comprehension iterates over each number in the numbers list. It applies the expression num**2 to each number and adds the result to the new list squared only if the number is greater than 5. The resulting squared list will contain the squares of numbers greater than 5, which are 49, 81, 100, and 144.

14. How do you implement multithreading in Python?

To enable multithreading in Python, you can utilize the threading module, which offers features for thread creation and management. Multithreading allows for the simultaneous execution of multiple threads within a single Python process.

To implement multithreading, follow these steps:

  • Import the threading module
  • Define a function that will run in a separate thread
  • Create a thread object and specify the function as the target
  • Start the thread’s execution using the start  method

It is important to be aware of Python’s Global Interpreter Lock (GIL), which may limit the parallel execution of multiple threads for CPU-bound tasks. However, threads can still provide benefits for I/O-bound operations, as the GIL is released during I/O operations.

In summary, by utilizing the threading module, you can introduce multithreading capabilities in Python, allowing for concurrent execution of threads and enhancing the performance of I/O-bound tasks.

15. What is the purpose of the 'self' parameter in Python class methods?

The “self” parameter in Python class methods serves as a reference to the class instance. It is a convention to name the first parameter of instance methods “self“, although you can choose any valid name.

  • The purpose of the “self” parameter is to access and manipulate the methods and attributes of the instance within the class. When a method is called on an instance of a class, the instance itself is automatically passed as the “self” parameter to the method.
  • By using the “self” parameter, you can access the instance’s attributes and invoke other methods belonging to the class. It allows you to differentiate between instance variables (attributes) and local variables within the method.

Advanced Deloitte Interview Questions

16. What is the purpose of the 'init' method in Python classes?

The purpose of the “init” method in Python classes is to initialize and set up the initial state of objects created from the class. It is a special method that gets automatically called when a new instance of the class is created.

The “init” method is often referred to as the constructor of the class. It allows you to define and assign initial values to the attributes (data members) of an object. Within the “init” method, you can specify parameters that will receive values when creating instances of the class, and you can perform any necessary setup or initialization operations. By implementing the “init” method, you ensure that essential attributes are properly initialized when creating objects, providing them with the necessary initial state. This allows for consistency and helps maintain the integrity of the objects throughout their lifecycle.

17. Describe the difference between 'is' and '==' in Python.

In Python, the “is” and “==operators are used for different comparison purposes. The “is” operator checks if two variables or objects refer to the same memory location, indicating that they are the same object in terms of identity. It evaluates to “True” if the operands are the same object and “False” otherwise. It compares the memory address rather than the values or content of the objects.

Example  - a = [1, 2, 3], b = [1, 2, 3]

print(a == b)  # Output: True, as a and b have the same value

On the other hand, the “==” operator is used to check if two variables or objects have the same value or content. It compares the values of the objects rather than their identities. It evaluates to “True” if the operands have the same value and “False” otherwise.


a = [1, 2, 3]
b = [1, 2, 3]

print(a == b)  # Output: True, as 'a' and 'b' have identical values

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18. What are the built-in data types in Python?

Python provides several built-in data types, including:

  1. Numeric types: int, float, complex
  2. Text type: str
  3. Sequence types: list, tuple, range
  4. Set types: set, frozenset
  5. Mapping type: dict
  6. Binary types: bytes, bytearray, memoryview
  7. Boolean type: bool

19. How do you handle memory management in Python?

In Python, memory management is handled automatically through a mechanism called “garbage collection”. Python’s garbage collector deallocates memory for objects that are no longer referenced by the program, freeing up resources. Developers do not need to manually allocate or deallocate memory as in languages like C or C++. Python’s memory management is transparent and efficient.

20. Describe the usage of Python's 'zip' function.

The “zip” function in Python is used to combine multiple iterables into a single iterable. It takes iterables as arguments and returns an iterator of tuples. Each tuple has elements from the corresponding positions of the input iterables. The resulting iterator stops when the shortest input iterable is exhausted.

Example: names = ['Robin', 'Barney', 'Ted']
ages = [30, 31, 32]
combined = zip(names, ages)

for name, age in combined:
    print(name, age)

In this example, the “zip” function combines the “names” and “ages” lists into an iterator of tuples. The “for” loop iterates over the tuples, and each tuple’s elements are unpacked into the variables “name” and “age” for printing.

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