Many job seekers face challenges when preparing for coding interviews, especially with companies like Accenture. The pressure to perform well can be overwhelming. Understanding the types of questions asked and the expected solutions is crucial for success.

This article provides a comprehensive guide to previous year coding questions and answers from Accenture. By reviewing these materials, candidates can enhance their coding skills and boost their confidence for the interview process.

Prepare effectively and increase your chances of landing a job at Accenture with the insights shared in this article.

## Accenture Coding Round Format

The Accenture coding round is a critical part of the recruitment process for candidates seeking technical roles. This round assesses a candidate’s programming skills, problem-solving abilities, and understanding of algorithms and data structures. Below is a detailed overview of the format of the Accenture coding round.

**Number of Questions**

In the Accenture coding round, candidates are required to solve two coding questions. This format is consistent across various recruitment cycles. Each question is designed to evaluate the candidate’s coding proficiency and logical reasoning.

**Time Allotted**

Candidates are given a total of 45 minutes to complete both coding questions. This time constraint adds pressure, requiring candidates to manage their time effectively. Each question is typically allocated approximately 22-23 minutes, which includes reading the problem statement, coding the solution, and testing the code.

**Allowed Programming Languages**

Candidates can use several programming languages to solve the coding questions. The preferred languages include:

- C
- C++
- Java
- Python
- .NET

This flexibility allows candidates to choose a language they are most comfortable with, which can enhance their performance during the coding round.

**Question Types**

The coding questions in this round focus on various topics, primarily involving:

- Algorithms: Candidates may be asked to implement sorting algorithms, searching algorithms, or other algorithmic challenges.
- Data Structures: Questions may involve the use of arrays, linked lists, trees, or graphs. Candidates should be familiar with the properties and operations of these structures.
- Problem-Solving: Candidates must demonstrate their ability to analyse problems and devise effective solutions. This may include real-world scenarios or abstract problems that require logical reasoning.

The difficulty level of the questions can vary. Typically, one question may be of medium difficulty, while the other is more challenging. Candidates should prepare for high-difficulty questions to succeed.

**Evaluation Criteria**

Accenture evaluates candidates based on several criteria during the coding round:

**Correctness:**The primary criterion is whether the code produces the correct output for the given input. Candidates must ensure their solutions meet the problem requirements.**Efficiency:**Candidates are expected to write efficient code. This includes considering time and space complexity. Solutions that are overly complex or inefficient may receive lower scores.**Code Readability:**The clarity of the code is also important. Candidates should write clean and well-structured code. Proper use of comments, meaningful variable names, and consistent formatting contribute to better readability.

To successfully clear the coding round, candidates must achieve one complete output and one partial output. This means that at least one of the solutions must be fully correct, while the other can be partially correct but must demonstrate a good understanding of the problem.

Now that you understand the format, letâ€™s delve into the common coding interview topics that Accenture focuses on. This knowledge will help you target your studies effectively.

## Accenture Common Coding Round Interview Topics

Accenture’s coding interview typically consists of two coding questions. Candidates have 45 minutes to complete these questions. The coding round evaluates problem-solving skills and coding proficiency. Candidates can use languages like C, C++, Java, Python, or Dot Net to write their solutions.

### Common Coding Topics

**1. Data Structures**

Data structures are essential for organising and storing data efficiently. Candidates should be familiar with:

- Arrays: Understand how to manipulate arrays, including sorting and searching.
- Linked Lists: Know how to implement singly and doubly linked lists and perform operations like insertion and deletion.
- Stacks and Queues: Understand how to use stacks for backtracking and queues for breadth-first search.
- Trees: Be able to traverse binary trees and implement operations on binary search trees.
- Graphs: Know basic graph algorithms like depth-first search (DFS) and breadth-first search (BFS).

**2. Algorithms**

Algorithms are crucial for solving problems efficiently. Key algorithms include:

- Sorting Algorithms: Be familiar with bubble sort, quicksort, and mergesort. Understand their time complexities.
- Searching Algorithms: Know linear search and binary search. Understand when to use each method.
- Dynamic Programming: Learn how to solve problems using dynamic programming techniques like memoization and tabulation.
- Recursion: Understand how to solve problems recursively and identify base cases.

**3. Problem-Solving Techniques**

Candidates should practise various problem-solving techniques. These include:

- Brute Force: Start with a straightforward solution and refine it.
- Greedy Algorithms: Make the locally optimal choice at each step with the hope of finding a global optimum.
- Backtracking: Use backtracking to solve problems like the N-Queens problem or Sudoku.

Next, we will review previous year coding interview questions and solutions. Analysing these can provide valuable insights into the types of questions you may encounter.

## Accenture Previous Year Coding Interview Questions and Solutions

Here are some of the most frequently asked Accenture coding interview questions from 2023, along with step-by-step solutions:

### 1) Find the maximum value and its index in an array

Given an array of integers, write a program to find the maximum value and its index in the array.

**Example**

text

Input: [5, 8, 3, 9, 6, 2]

Output:

Maximum value: 9

Index: 3

**Solution**

python

def find_max(arr):

max_val = arr[0]

max_index = 0

for i in range(1, len(arr)):

if arr[i] > max_val:

max_val = arr[i]

max_index = i

return max_val, max_index

# Example usage

arr = [5, 8, 3, 9, 6, 2]

result = find_max(arr)

print(“Maximum value:”, result[0])

print(“Index:”, result[1])

This solution iterates through the array, keeping track of the maximum value and its index seen so far. It returns a tuple containing the maximum value and its index.

### 2) Reverse a linked list

Given the head of a singly linked list, write a program to reverse the linked list.

**Example**

text

Input: 1 -> 2 -> 3 -> 4 -> NULL

Output: 4 -> 3 -> 2 -> 1 -> NULL

**Solution**

python

class ListNode:

def __init__(self, val=0, next=None):

self.val = val

self.next = next

def reverse_list(head):

prev = None

curr = head

while curr is not None:

next_node = curr.next

curr.next = prev

prev = curr

curr = next_node

return prev

# Example usage

# Create the linked list: 1 -> 2 -> 3 -> 4 -> NULL

node4 = ListNode(4)

node3 = ListNode(3, node4)

node2 = ListNode(2, node3)

node1 = ListNode(1, node2)

reversed_head = reverse_list(node1)

# Print the reversed linked list

current = reversed_head

while current is not None:

print(current.val, end=” “)

current = current.next

This solution uses a three-pointer approach to reverse the linked list. It iterates through the list, updating the pointers to reverse the links 2.

### 3) Implement a stack using an array

Write a program to implement a stack using an array. Include methods for pushing, popping, and checking the top element.

**Solution**

python

class Stack:

def __init__(self):

self.stack = []

def push(self, element):

self.stack.append(element)

def pop(self):

if not self.is_empty():

return self.stack.pop()

else:

return “Stack is empty”

def top(self):

if not self.is_empty():

return self.stack[-1]

else:

return “Stack is empty”

def is_empty(self):

return len(self.stack) == 0

def __str__(self):

return str(self.stack)

# Example usage

stack = Stack()

stack.push(1)

stack.push(2)

stack.push(3)

print(stack) # Output: [1, 2, 3]

print(stack.top()) # Output: 3

print(stack.pop()) # Output: 3

print(stack) # Output: [1, 2]

This solution uses a Python list to implement the stack. It includes methods for pushing, popping, checking the top element, and checking if the stack is empty.

### 4) Implement a queue using an array

Write a program to implement a queue using an array. Include methods for enqueuing, dequeuing, and checking the front element.

**Solution**

python

class Queue:

def __init__(self):

self.queue = []

def enqueue(self, element):

self.queue.append(element)

def dequeue(self):

if not self.is_empty():

return self.queue.pop(0)

else:

return “Queue is empty”

def front(self):

if not self.is_empty():

return self.queue[0]

else:

return “Queue is empty”

def is_empty(self):

return len(self.queue) == 0

def __str__(self):

return str(self.queue)

# Example usage

queue = Queue()

queue.enqueue(1)

queue.enqueue(2)

queue.enqueue(3)

print(queue) # Output: [1, 2, 3]

print(queue.front()) # Output: 1

print(queue.dequeue()) # Output: 1

print(queue) # Output: [2, 3]

This solution uses a Python list to implement the queue. It includes methods for enqueuing, dequeuing, checking the front element, and checking if the queue is empty.

### 5) Find the first non-repeating character in a string

Given a string, write a program to find the first non-repeating character in the string.

**Example**

text

Input: “hello”

Output: “h”

**Solution**

python

def first_non_repeating_char(s):

char_count = {}

# Count the frequency of each character

for char in s:

if char in char_count:

char_count[char] += 1

else:

char_count[char] = 1

# Find the first non-repeating character

for char in s:

if char_count[char] == 1:

return char

return None

# Example usage

string = “hello”

result = first_non_repeating_char(string)

print(result) # Output: h

This solution uses a dictionary to count the frequency of each character in the string. It then iterates through the string again to find the first character with a frequency of 1.

** Disclaimer:** These are just a few examples of the types of coding questions you may encounter in Accenture coding interviews. It’s important to practise a wide range of questions, understand the underlying concepts, and be able to explain your thought process and solutions clearly.

After reviewing these questions, we will share some tips and tricks to help you excel in the Accenture coding round. These strategies can boost your confidence and performance.

## Tips and Tricks for Accenture Coding Round Questions

In this section, we will provide practical tips to enhance your performance in the coding round. These include time management strategies, coding best practices, and ways to tackle difficult problems. Implementing these tips can lead to better results:

### 1) General Problem-Solving Strategies

The Accenture coding round tests your problem-solving skills. Break down problems into smaller steps. Understand the problem completely before writing code. Write down examples to clarify inputs and outputs. Think about different solutions. Choose the best approach based on time and space complexity. Practise regularly to improve your problem-solving speed and accuracy.

### 2) Importance of Code Optimization

Optimised code runs faster and uses less memory. Identify performance bottlenecks in your code. Use efficient algorithms and data structures. Avoid unnecessary calculations. Test your code with large inputs to check performance. Optimise for time or space based on problem requirements.

### 3) Effective Time Management During the Coding Round

Time management is crucial. Read all problems before starting. Allocate time based on problem difficulty. Focus on completing one problem at a time. Avoid getting stuck on a single problem. Use code comments to explain your logic. Review your code for errors before submitting.

### 4) Debugging Techniques

Debugging is essential for finding and fixing errors. Print values to check intermediate results. Use a debugger to step through code line by line. Test your code with different inputs. Break down the problem into smaller functions for easier debugging. Learn common error types and how to fix them.

### 5) How to Approach Different Types of Questions

Practise different problem types. Learn common algorithms and data structures. Understand time and space complexity for each approach. Break down problems into subproblems. Think about edge cases and handle them correctly. Write clean and readable code.

## Conclusion

Understanding past Accenture coding questions helps job seekers prepare better. This article gave you a taste of what to expect. Remember, practice makes perfect. To ace your Accenture coding interview, try solving many coding problems.

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