Accenture Python Language Questions

Last Updated on August 21, 2024 by Abhishek Sharma

As Python continues to dominate the programming world with its simplicity and versatility, it’s no surprise that Accenture, a leading global professional services company, emphasizes Python in its technical assessments. Whether you’re applying for a software development role, data science position, or any technical role, having a solid grasp of Python is crucial. Accenture’s Python language questions are designed to test your problem-solving skills, coding efficiency, and understanding of key Python concepts. This article provides an overview of what to expect from Python-related questions during Accenture’s recruitment process, along with tips to help you prepare effectively.

Accenture has recently focused on expanding its capabilities in digital technology and innovation. In 2017, the company introduced the "Accenture Innovation Architecture," which is a set of tools, methods, and assets that help clients drive innovation and transformation in their businesses.

Accenture Python Language Questions

Here are a list of some python language questions commonly asked in Accenture interview:

Question 1) Why Python?
Answer: Python is a popular programming language for a variety of reasons, including

• Easy to Learn and Use: Python has a clear and concise syntax that is easy to understand and read, making it an ideal language for beginners. It also has a vast community of developers who create and share resources to help others learn and improve their skills.
• Versatile: Python can be used for a wide range of applications, including web development, data analysis, machine learning, scientific computing, and more. It has a vast array of libraries and frameworks that make it easy to perform complex tasks quickly and efficiently.
• Large Community and Support: Python has a large and active community of developers who share their knowledge and resources, making it easy to get help and find solutions to problems. There are also many forums, blogs, and websites dedicated to Python, making it easy to find information and stay up-to-date with the latest trends.
• Portable: Python can run on different platforms, including Windows, macOS, and Linux, which makes it easier to develop and deploy code across different systems.
• Open-Source: Python is an open-source programming language, which means that anyone can download and use it for free. It also means that developers can contribute to the language’s development and improve it over time.

Overall, Python is a powerful and versatile language that is easy to learn and has a supportive community, making it an excellent choice for both beginners and experienced programmers.

Question 2) In a Python Application, How to find Bugs or perform Static Analysis?
Answer: There are various tools and techniques that can be used to find bugs and perform static analysis in a Python application. Here are some suggestions:

• Use a linter: A linter is a tool that analyzes your code for potential errors, style violations, and syntax issues. Some popular Python linters include pylint, flake8, and Pyflakes.
• Use a debugger: A debugger allows you to step through your code and track down issues. Python comes with a built-in debugger called pdb, and there are also third-party debuggers like PyCharm.
• Use a code profiler: A code profiler can help you identify performance bottlenecks in your code. Python includes a built-in profiler called cProfile, and there are also third-party profilers like Pyflame.
• Use a testing framework: Writing tests for your code can help you identify issues before they become problems. Popular Python testing frameworks include unittest, pytest, and nose.
• Use a code review process: Having other developers review your code can help you identify issues that you may have missed. Code review tools like GitHub or Bitbucket can help you automate the review process.
• Use a static code analysis tool: Static code analysis tools can help you identify potential issues in your code before it’s even executed. Popular Python static code analysis tools include SonarQube, Pylint, and Prospector.

By incorporating some or all of these tools and techniques into your development process, you can improve the quality of your Python code and catch bugs and errors before they become a problem.

Question 3) When is Python Decorator used?
Answer: Python decorators are used to modify or extend the behavior of functions, methods, or classes. They are essentially functions that take another function or class as input and return a new function or class with added functionality.

Here are some common use cases for Python decorators:

• Logging: A decorator can be used to log function calls, arguments, and return values.
• Timing: A decorator can be used to time the execution of a function or method.
• Authorization and Authentication: A decorator can be used to check if a user is authorized to access a function or method.
• Caching: A decorator can be used to cache the result of a function or method to avoid redundant computation.
• Validation: A decorator can be used to validate the input arguments of a function or method.
• Error Handling: A decorator can be used to catch exceptions raised by a function or method and handle them appropriately.
• Debugging: A decorator can be used to print debug information, such as function arguments and local variables.

Python decorators provide a powerful way to extend the functionality of existing functions and methods without modifying their code directly. They allow you to add reusable, modular features to your codebase and can help to improve code organization and readability.

Question 4) What do you mean by Python literals?
Answer: In Python, literals are the raw values or data that are used to represent a specific type of data. They are the fixed values that are used to directly assign to variables or to be used as inputs in expressions and operations. In other words, literals are the fundamental building blocks of Python programs.

Python supports various types of literals, including:

• Numeric literals, such as integers, floating-point numbers, and complex numbers
• String literals, which are used to represent textual data
• Boolean literals, which are used to represent the truth values of True and False
• Sequence literals, such as lists, tuples, and range objects
• Mapping literals, such as dictionaries
• Set literals, which are used to represent sets of unique elements
• None literal, which is used to represent the absence of a value

Question 5) Write a program which Check if a string can be obtained by rotating another string by 2 places.

Input

str1 = "prepbytes"
str2 = "epbytespr"

Answer:

def isRotated(str1, str2):

    if (len(str1) != len(str2)):
        return False
    
    if(len(str1) < 2):
        return str1 == str2
    clock_rot = ""
    anticlock_rot = ""
    l = len(str2)

    #  anti-clockwise rotation
    anticlock_rot = (anticlock_rot + str2[l - 2:] +
                                    str2[0: l - 2])
    
    #  clock wise rotation
    clock_rot = clock_rot + str2[2:] + str2[0:2]


    return (str1 == clock_rot or
            str1 == anticlock_rot)

# Driver code
if __name__ == "__main__":
    
    str1 = "prepbytes"
    str2 = "epbytespr"
if isRotated(str1, str2):
    print("Yes")
else:
    print("No")

Output

Yes

Question 6) What will be the output of given code ?

x=8
y=3
while(x<11):
        print(x)
        x+=1

Answer: The output will be

8
9
10

Question 7) Which python library is used for Machine Learning?
Answer: The most popular Python library used for machine learning is scikit-learn, also known as sklearn. Scikit-learn is a free, open-source machine learning library that provides simple and efficient tools for data mining and data analysis. It includes a wide range of machine learning algorithms for tasks such as classification, regression, clustering, and dimensionality reduction, as well as tools for model selection and evaluation, feature extraction, and data preprocessing.

Other popular libraries for machine learning in Python include TensorFlow, Keras, PyTorch, and Theano, which are primarily used for deep learning and neural networks, as well as Pandas and NumPy, which are used for data manipulation and analysis.

Question 8) What is the difference between the remove() function and the del statement?
Answer: Here are the main differences between the remove() function and the del statement in Python:

remove() function:

• Removes the first occurrence of a specified element from a list based on its value.
• Modifies the list in place and returns the modified list.
• Raises a ValueError if the specified element is not found in the list.

del statement:

• Removes a specific element from a list based on its index.
• Modifies the list in place and does not return anything.
• Raises an IndexError if the specified index is out of range.

Question 9) What is the output of print tuple if tuple = ( ‘abcd’, 786 , 2.23, ‘john’, 70.2 )?
Answer: The output is

('abcd', 786, 2.23, 'john', 70.2)

Question 10) What is NumPy and How is it Better than a List in Python?
Answer: NumPy is a popular numerical computing library for Python that provides support for large, multi-dimensional arrays and matrices, as well as a variety of mathematical operations to work with them efficiently.

Compared to Python's built-in list data type, NumPy offers several advantages for numerical computing:

• Performance: NumPy arrays are significantly faster than Python lists for numerical operations because they are implemented in C, which allows for efficient memory management and vectorization.
• Memory efficiency: NumPy arrays are more memory-efficient than Python lists because they are stored in contiguous memory blocks, which makes them faster to access and manipulate.
• Broadcasting: NumPy provides broadcasting capabilities that allow you to perform operations between arrays of different shapes and sizes, which can be very useful in numerical computing.
• Functions: NumPy provides a wide range of functions that are optimized for numerical operations, such as element-wise operations, linear algebra operations, and statistical operations.
• Interoperability: NumPy arrays can be seamlessly integrated with other libraries that are built for numerical computing, such as SciPy, Pandas, and Matplotlib.

Overall, NumPy is a powerful tool for numerical computing in Python that provides better performance, memory efficiency, and functionality compared to Python's built-in list data type.

Question 11) Is python a case-sensitive language?
Answer: Yes, Python is a case-sensitive language. This means that Python distinguishes between uppercase and lowercase letters in its syntax. For example, the variable names "myVar" and "myvar" are considered different variables in Python, and referring to them in the wrong case can lead to errors in your code.

Question 12) What is a tuple in Python?
Answer: In Python, a tuple is an ordered collection of values, similar to a list. However, tuples are immutable, which means that once a tuple is created, its values cannot be changed. Tuples are often used to group related values together, particularly when those values are not expected to change. They can also be used as keys in dictionaries, which require immutable objects as keys.

Question 13) What are the different file processing modes supported by Python?
Answer: Python supports several file processing modes that determine how a file can be opened and read or written to. The different file processing modes in Python are:

• "r" (Read mode): This is the default mode and it is used to open a file for reading. You can read the contents of the file, but you cannot modify it.
• "w" (Write mode): This mode is used to open a file for writing. If the file already exists, its contents will be erased. If the file does not exist, it will be created.
• "a" (Append mode): This mode is used to open a file for appending. If the file exists, new data will be written to the end of the file. If the file does not exist, it will be created.
• "x" (Exclusive creation mode): This mode is used to create a new file. If the file already exists, an error will be raised.
• "b" (Binary mode): This mode is used to open a file in binary mode, which means that the file will be read or written as a binary file. This mode is used when working with non-text files such as images, audio files, and videos.
• "t" (Text mode): This mode is used to open a file in text mode, which means that the file will be read or written as a text file. This mode is used when working with text files such as .txt files.

These modes can be combined to create different types of file processing modes. For example, "rb" can be used to open a file in binary mode for reading, and "wt" can be used to open a file in text mode for writing.

Question 14) Are there any interfaces to database packages in Python?
Answer: Yes, there are many interfaces to database packages in Python. Python provides several modules for working with databases, which make it easy to interact with a wide variety of database systems from within Python.

Here are some of the most popular Python database modules:

• SQLite3: This module provides an interface to the SQLite database system, which is a lightweight, file-based database that is often used for small-scale applications.
• MySQL Connector/Python: This module provides an interface to the MySQL database system, which is one of the most widely used open-source relational database systems.
• psycopg2: This module provides an interface to the PostgreSQL database system, which is another popular open-source relational database system.
• Oracle Database: This module provides an interface to the Oracle Database system, which is a commercial relational database system used by many large-scale applications.
• PyMongo: This module provides an interface to the MongoDB NoSQL database system, which is a document-oriented database used for storing large volumes of unstructured data.

These modules allow you to connect to and interact with databases from within your Python programs. You can execute SQL queries, insert data into tables, update data, and more, all from within your Python code.

Question 15) Tell the output of the following code.

import array  
list1 = [1, 4, 5, 10, 12, 71]  
print(list1[-3])  
print(list1[-5])  
print(list1[-1])  

Answer:

Output

10
4
71

Conclusion
Mastering Python is essential for candidates aspiring to secure a technical position at Accenture. The Python language questions in Accenture’s assessments are designed to evaluate not just your coding ability, but also your problem-solving approach and understanding of fundamental concepts. By thoroughly preparing for these questions and practicing Python regularly, you can enhance your chances of performing well in the technical assessments and securing a position at Accenture.

Frequently Asked Questions(FAQ) related to Accenture Python Language Questions

Here are some FAQs related to Accenture Python Language Questions:

Q1: What types of Python questions are asked in Accenture’s technical assessments?
A: Accenture’s Python questions typically include coding problems that test your ability to implement algorithms, work with data structures, and handle file operations. You may also encounter questions on Python syntax, libraries, and error handling.

Q2: Are the Python questions in Accenture’s exam difficult?
A: The difficulty level of Python questions can vary, ranging from basic to intermediate. The questions are designed to assess both your theoretical knowledge and practical coding skills, so it’s important to be well-prepared.

Q3: How much time is allotted for Python coding questions during the Accenture exam?
A: The time allotted can vary depending on the specific assessment format. However, candidates are typically given 30 to 60 minutes to solve Python coding problems, depending on the number and complexity of the questions.

Q4: Do I need to know advanced Python topics for Accenture’s assessment?
A: While a strong understanding of basic to intermediate Python concepts is essential, knowing advanced topics can give you an edge. Focus on areas like data structures, algorithms, and common libraries (e.g., NumPy, Pandas) relevant to the role you’re applying for.

Q5: Can I use Python libraries during the coding assessment?
A: Yes, you can use standard Python libraries that are commonly available in coding environments. However, it’s important to read the instructions provided during the exam to ensure compliance with any specific guidelines.