Table of Contents
Preparing for a Python-related role at Accenture requires more than memorising definitions. Depending on the role, you may face an online assessment, coding problems, questions on Python fundamentals, data structures, SQL/DBMS, operating systems, networking, and a discussion of your projects.
This guide covers commonly asked Python concepts, explains how to answer them clearly, and highlights the topics you should practise before an Accenture interview. The exact selection process and technical depth can vary by role, experience level, location, and hiring campaign.
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Introduction: About Accenture
Accenture is a global professional services company that provides consulting, technology, and operations services. It works with clients across industries on digital transformation, cloud, data, AI, cybersecurity, and technology-enabled business transformation. Accenture possesses expertise in numerous crucial business domains, such as outsourcing, technology, business strategy, supply chain management, and customer relationship management.
Accenture’s origins trace back to Andersen Consulting, which rebranded as Accenture in 2001. The worldwide head office of Accenture is located in Dublin, Ireland. It operates branch offices in over 200 places in 50 different nations.
Accenture excels at identifying emerging business and technological trends by applying its industry expertise and technological prowess. This allows the company to create solid solutions that support clients globally in expanding into new markets or boosting sales in current ones. Accenture also prioritises enhancing operational performance and advising customers on how to provide goods and services more effectively and efficiently.
Accenture serves clients across many industries and is a major employer for technology, consulting, data, cloud, and digital roles.
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Why Accenture?
Accenture hires Python professionals for roles in application development, data engineering, data analysis, QA and automation, cloud engineering, AI/ML, and digital transformation. Python may be used for backend services, automation, data processing, APIs, testing, analytics, and machine-learning workflows.
Before applying, review the specific job description. A Python developer role may emphasise frameworks such as Django or FastAPI, while a data role may emphasise SQL, Pandas, NumPy, cloud platforms, and data pipelines.
Python Roles and Skills at Accenture
1: Which of the following data types is immutable in Python?
Python-related opportunities at Accenture can vary by business unit and client engagement. Common areas include:
- Application development: Python, APIs, web frameworks, databases, cloud services, and testing.
- Data engineering and analytics: SQL, Python, Pandas, NumPy, ETL concepts, data visualisation, and cloud data platforms.
- AI and machine learning: Python, Pandas, NumPy, scikit-learn, model evaluation, and responsible AI practices.
- Quality engineering and automation: Python, Selenium or Playwright, API testing, CI/CD, and test automation.
- Cloud and DevOps: Python scripting, Linux, Git, Docker, CI/CD, cloud services, and infrastructure automation.
For fresher roles, employers commonly look for strong programming fundamentals, problem-solving, communication, willingness to learn, and awareness of core computer-science concepts.
Accenture Python Interview Questions – Download PDF
Accenture Interview Process
1. Interview Process
Do not assume that every applicant will follow the same number of rounds. Read the invitation email carefully, confirm the assessment format, and prepare for both technical and behavioural questions.
2. Interview Rounds
Online Test:
The online assessment may include cognitive and communication components, role-relevant technical questions, pseudocode, and coding tasks. The exact format depends on the role and hiring campaign.
Practise under timed conditions. For Python-focused roles, revise core syntax, data structures, algorithms, complexity analysis, SQL, operating-system basics, networking basics, and cloud/security fundamentals.
Technical Interview:
You will be contacted for the technical interview if you pass the online exam. The primary goals of the technical round are to assess the candidate’s coding and problem-solving abilities. To ace the technical interview, candidates should possess a fundamental understanding of data structures, algorithms, and other computer science-related topics, including OS, DBMS, CN, etc.
Expect to be tested in the appropriate areas based on the job role you are applying for. You may need to go through two or more rounds of technical interviews if you are an experienced applicant or are looking for a crucial position.
What to prepare:
- Python fundamentals: data types, functions, control flow, exception handling, comprehensions, modules, and virtual environments.
- OOP: classes, objects, inheritance, polymorphism, encapsulation, dunder methods, and composition.
- Data structures and algorithms: arrays, strings, hash maps, sets, stacks, queues, recursion, sorting, searching, and time/space complexity.
- SQL and databases: joins, grouping, aggregation, subqueries, indexes, normalisation, and basic database design.
- Projects: architecture, your specific contribution, challenges, debugging, trade-offs, and results.
- Communication: explain your approach before coding, state assumptions, test edge cases, and discuss improvements.
HR Interview:
The HR interview is the last stage in the hiring process at Accenture since it helps ascertain a candidate’s personality and other relevant factors. Here, you may be asked a wide range of questions about yourself, such as your background, education, work history, interests, and weaknesses, as well as your expectations for a wage. Questions on Accenture’s business may also be asked during this round.
The purpose of an HR interview is to determine whether you are a good fit for the organisation, assess your personality, review your background, and determine whether you can handle the job role. The other interview rounds at Accenture are designed to evaluate your abilities and accomplishments.
Prepare concise examples using the STAR method: Situation, Task, Action, and Result. Be ready to discuss teamwork, conflict resolution, deadlines, learning a new technology, handling feedback, and a project where you solved a real problem.
Also prepare a clear answer for why you want to join Accenture, based on the specific role and business area—not a generic response about brand reputation.
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Access Free Python MaterialTop Accenture Python Interview Questions
Q1. What is Python? List some popular applications of Python in the world of technology.
Answer:
Python is a high-level, interpreted, general-purpose programming language known for readable syntax and a large ecosystem of libraries. It supports multiple programming styles, including procedural, object-oriented, and functional programming.
Python is widely used for web development, automation, data analysis, machine learning, scientific computing, scripting, APIs, testing, and cloud development. Popular libraries and frameworks include Pandas, NumPy, Django, FastAPI, Flask, Requests, and PyTest.
Q2. Importance of self-keywords in Python?
Answer:
self is the conventional name for the first parameter of an instance method in a Python class. It refers to the specific object on which the method is called.
Python passes the instance automatically when you call an instance method. Although self is only a convention—not a reserved keyword—using it improves readability and consistency.
Example
classEmployee:def__init__(self, name): self.name = name defgreet(self):returnf"Hello, {self.name}" emp = Employee("Asha")print(emp.greet())# Hello, AshaQ3. Define NumPy in Python?
Answer:
NumPy is a Python library for numerical and scientific computing. Its core feature is the ndarray, an efficient multidimensional array that supports vectorised operations, broadcasting, slicing, and mathematical functions.
NumPy is commonly used in data analysis, machine learning, image processing, and scientific applications. It is also a foundation for libraries such as Pandas, SciPy, and many machine-learning frameworks.
Q4. What does “name mangling” in Python mean?
Answer:
Name mangling occurs when an attribute name begins with two underscores, such as __salary, inside a class. Python changes its internal name to _ClassName__salary to reduce accidental access or overriding in subclasses.
It is not true privacy or security. The attribute can still be accessed using the mangled name, so Python treats a single underscore as a convention for “internal use” and double underscores as a convention for avoiding name clashes.
class Employee:
def __init__(self):
self.__salary = 50000
emp = Employee()
print(emp._Employee__salary) # 50000
Q5. Why is Python unable to support function overloading?
Answer:
Python does not support traditional compile-time function overloading based on different parameter signatures. If you define multiple functions with the same name, the latest definition replaces the earlier ones.
However, Python supports flexible function design through default arguments, keyword arguments, *args, **kwargs, and functools.singledispatch. These approaches let a single function handle different types or numbers of inputs.
Example
from functools import singledispatch @singledispatchdefprocess(value):returnf"Generic value: {value}"@process.registerdef_(value:int):returnf"Integer: {value *2}"@process.registerdef_(value:str):returnf"String: {value.upper()}"print(process(5))# Integer: 10print(process("hi"))# String: HIQ6. For machine learning, which Python library is used?
Answer:
scikit-learn is one of the most widely used Python libraries for classical machine learning. It provides tools for regression, classification, clustering, dimensionality reduction, model selection, preprocessing, and evaluation.
Along with tools for feature extraction, data preparation, model selection and evaluation, it offers a broad range of machine learning algorithms for applications including regression, classification, clustering, and dimensionality reduction.
Q7. Does Python is case sensitive?
Answer: Python is case-sensitive. Identifiers such as myVar, MyVar, and myvar are treated as different names. This applies to variables, functions, classes, modules, and keywords.
Q8. In Python, what is a tuple?
Answer:
A tuple is an ordered, immutable collection of values. Once created, its elements cannot be added, removed, or replaced directly. Tuples are useful for fixed groups of related values, such as coordinates or database records.
A tuple can be used as a dictionary key only if all of its elements are hashable. For example, a tuple containing a list cannot be used as a dictionary key because lists are mutable and unhashable.
Example
location =(12.9716,77.5946) employee ={"name":"Ravi","skills":("Python","SQL")}Q9. Which various file processing modes does Python support?
Answer:
Python file modes determine how a file is opened:
r: Read mode; the file must exist.w: Write mode; creates the file or truncates an existing file.a: Append mode; creates the file if needed or adds data at the end.x: Exclusive creation mode; fails if the file already exists.r+: Read and write mode; the file must exist.w+: Read and write mode; truncates an existing file or creates a new one.a+: Read and append mode; creates the file if needed.
t means text mode and b means binary mode. They are modifiers, not standalone modes: for example, rb opens a file for reading in binary mode.
Example
withopen("notes.txt","a", encoding="utf-8")asfile:file.write("New line\n")Q10. Do Python database packages have any interfaces?
Answer:
Python can connect to many databases through dedicated drivers and ORM libraries. Common options include:
sqlite3: Built-in interface for SQLite.mysql-connector-pythonorPyMySQL: MySQL.psycopg/psycopg2: PostgreSQL.oracledb: Oracle Database.pymongo: MongoDB.- SQLAlchemy: A popular ORM and database toolkit that can work with several relational databases.
For interviews, also be prepared to write SQL queries involving JOIN, GROUP BY, HAVING, subqueries, indexes, and aggregate functions.
Q11. In Python, how is memory managed?
Answer:
Python manages memory through a private heap where objects and data structures are stored. The Python memory manager handles allocation and deallocation at a high level.
CPython primarily uses reference counting to reclaim objects when their reference count reaches zero. It also has a cyclic garbage collector that identifies and collects groups of objects involved in reference cycles. Programmers generally do not manually free memory as in C or C++.
Q12. Explain PYTHONPATH.
Answer:
PYTHONPATH is an environment variable that adds directories to Python’s module search path. When you import a module, Python searches the current directory, built-in modules, and directories listed in PYTHONPATH, along with installation-specific site-packages locations.
In modern Python projects, virtual environments and dependency files such as requirements.txt or pyproject.toml are usually preferred for managing project dependencies.
Q13. How to install Python on Windows and set path variables?
Answer:
- Download the latest Python installer from the official Python website: https://www.python.org/downloads/
- Run the installer and select Add Python to PATH during installation.
- Open Command Prompt and verify the installation:
python --version- If Python is not recognised, reinstall it with Add Python to PATH selected, or add the Python installation directory to the system
Pathenvironment variable manually. - Create an isolated project environment:
python -m venv venv venv\Scripts\activate pip install -r requirements.txtQ14. What are the types of literals in Python?
Answer:
Python literals are fixed values written directly in source code. Major categories include:
- Numeric literals: integers, floating-point numbers, and complex numbers, such as
10,3.14, and2 + 3j. - String literals: text enclosed in single, double, or triple quotes, such as
'Python',"data", and"""multi-line text""". - Bytes literals: such as
b"hello". - Boolean literals:
TrueandFalse. Noneliteral: represents the absence of a value.- Collection literals: lists, tuples, sets, and dictionaries, such as
[1, 2],(1, 2),{1, 2}, and{"key": "value"}.
Python does not have a separate “character literal” type; a single-character string such as "A" is simply a string.
Q15. Differentiate between range and xrange.
Answer:
In Python 3, range() returns a lazy sequence object rather than creating every number in memory at once. It generates values only when needed, making it memory-efficient for large ranges.
A list stores all its elements in memory immediately. Use range() when iterating over a sequence of numbers; convert it with list(range(n)) only when you genuinely need a list.
Example
numbers =range(1_000_000)print(numbers)# range objectprint(list(numbers[:5]))# [0, 1, 2, 3, 4]Q16. How do you copy an object in Python?
Answer:
A shallow copy creates a new outer object but copies references to the inner objects. Changing a nested mutable object through either copy can affect the other.
A deep copy recursively creates copies of nested objects as well, so changes to nested objects in one copy do not affect the other.
from copy import copy, deepcopy original =[1,2,[3,4]] shallow = copy(original) shallow.append(5)print(original)# [1, 2, [3, 4, 5]] deep = deepcopy(original) deep.append(6)print(original)# [1, 2, [3, 4, 5]]Q17. How are arguments passed in Python?
Answer:
Python uses pass-by-object-reference, often described as “call by sharing.” When you pass an argument, the function receives a reference to the same object, not a copy of the object.
If the object is mutable, such as a list or dictionary, changes made through the reference can be visible outside the function. If the function rebinds the parameter to a new object, the original variable outside the function is not affected.
defappend_item(items): items.append(4)defreassign(items): items =[9,10] numbers =[1,2,3] append_item(numbers)print(numbers)# [1, 2, 3, 4] reassign(numbers)print(numbers)# [1, 2, 3, 4]Q18. Why isn’t all the memory de-allocated when Python exits?
Answer:
When a Python program exits, the interpreter attempts to clean up objects and release resources. However, some memory may remain allocated because of reference cycles, objects held by extension modules, or memory managed by underlying C libraries.
For interview purposes, focus on reference counting, cyclic garbage collection, context managers, and explicitly closing files or network connections.
Q19. What is a dictionary in Python?
Answer:
A dictionary is a built-in mutable, unordered-by-default mapping that stores key-value pairs. Keys must be hashable and unique; values can be any Python object.
Since Python 3.7, dictionaries preserve insertion order as an implementation guarantee. Dictionaries provide efficient average-case lookup, insertion, and deletion by key.
Example
employee ={"name":"Priya","role":"Python Developer"} employee["role"]="Data Engineer"print(employee.get("salary","Not specified"))Q20. Explain the split(), sub(), and subn() methods of the Python “re” module.
Answer:
The re module supports regular-expression operations in Python:
re.split(pattern, string): Splits a string using a regex pattern and returns a list.re.sub(pattern, replacement, string): Replaces matched substrings and returns the modified string.re.subn(pattern, replacement, string): Performs the same replacement assub()but returns a tuple containing the new string and the number of substitutions.
import re text ="Python3 and Python2"print(re.sub(r"Python\d","Python", text))# Python and Pythonprint(re.subn(r"Python\d","Python", text))# ('Python and Python', 2)Q21. What are negative indexes and why do we utilize them?
Answer:
Negative indexes access elements from the end of a sequence. -1 refers to the last element, -2 to the second-last element, and so on.
They are useful for accessing trailing elements without calculating the sequence length.
languages =["Python","SQL","Java","C++"]print(languages[-1])# C++print(languages[-2])# Javaprint(languages[:-1])# ['Python', 'SQL', 'Java']Q22. Is there an object-oriented Programming (OOps) concept in Python?
Answer:
Python supports object-oriented programming. It provides classes, objects, inheritance, encapsulation, polymorphism, and composition. Python also supports procedural and functional programming styles.
In an interview, be ready to explain:
- Class and object: A class is a blueprint; an object is an instance of that class.
- Inheritance: A child class can reuse and extend a parent class.
- Polymorphism: Different objects can respond to the same method name in different ways.
- Encapsulation: Attributes and implementation details can be kept internal through conventions and name mangling.
- Composition: An object contains or uses other objects rather than inheriting from them.
Q24. How is multithreading achieved in Python?
Answer:
Python supports multithreading through the threading module and higher-level concurrent.futures.ThreadPoolExecutor. In CPython, the Global Interpreter Lock (GIL) allows only one thread to execute Python bytecode at a time.
Therefore, threads can improve performance for I/O-bound tasks, such as network requests, file operations, or database calls, because threads can wait for I/O while other threads run. For CPU-bound tasks, consider multiprocessing, process pools, or libraries that release the GIL during heavy computation.
from concurrent.futures import ThreadPoolExecutor import requests urls =["https://example.com","https://example.org"]with ThreadPoolExecutor(max_workers=2)as executor: results = executor.map(requests.get, urls)Q25. What are the different types of inheritance in Python?
Answer:
Python supports several inheritance patterns:
- Single inheritance: A class inherits from one parent class.
- Multiple inheritance: A class inherits from more than one parent class.
- Multilevel inheritance: A class inherits from a class that itself inherits from another class.
- Hierarchical inheritance: Multiple child classes inherit from the same parent class.
- Hybrid inheritance: A combination of two or more inheritance patterns.
Python resolves method and attribute lookup using the Method Resolution Order (MRO), which can be inspected with ClassName.__mro__.
classA:passclassB(A):passclassC(B):passprint(C.__mro__)Accenture Python Interview Questions: Conclusion
Python interviews at Accenture generally test whether you can apply fundamentals to real problems, write clean code, explain your reasoning, and work with databases, APIs, and projects. Focus on Python fundamentals, data structures and algorithms, SQL, OOP, debugging, and clear communication.
Practise explaining your project decisions, writing code with edge cases in mind, and discussing trade-offs. A structured preparation plan is more effective than memorising answers.
Experience the power of our Python programming course with a free demo – enroll now!
Master Python with Free MCQs & Tutorials!
Learn, practise, and level up your coding skills at zero cost, from basics to interview-ready concepts.
Access Free Python MaterialFrequently Asked Questions
What should I expect in an Accenture Python interview?
Expect questions on Python fundamentals, coding and problem-solving, data structures, SQL or databases, projects, and sometimes computer-science fundamentals such as operating systems and networking. For experienced roles, expect deeper questions on system design, cloud, testing, frameworks, or the technologies listed in the job description.
Do I need to have experience with specific Python libraries to pass an Accenture Python interview?
It depends on the role. Application-development roles may require Django, Flask, FastAPI, REST APIs, and databases. Data roles may require Pandas, NumPy, SQL, and visualisation tools. AI/ML roles may require scikit-learn, TensorFlow, or PyTorch. Read the job description carefully and prepare examples using the relevant tools.
What type of questions are asked in an Accenture Python interview?
You may face conceptual questions, output-prediction questions, live coding, debugging, SQL queries, project discussions, and behavioural questions. Interviewers often assess how you approach a problem, handle edge cases, and communicate your reasoning.
How should I prepare for an Accenture Python interview?
Build a preparation plan covering Python fundamentals, data structures and algorithms, SQL, OOP, projects, and mock interviews. Practise writing code without an IDE, explaining your approach aloud, testing edge cases, and discussing time and space complexity.
