AT&T Software Engineer Interview Questions

AT&T Software Engineer Interview Questions

On October 23, 2025, Posted by , In Interview Questions, With Comments Off on AT&T Software Engineer Interview Questions

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When preparing for an AT&T Software Engineer interview, I know how crucial it is to be ready for the diverse challenges they present. AT&T is renowned for its innovation, and they seek candidates who excel in areas like data structures, algorithms, system design, and coding proficiency in languages such as Java, Python, or C++. Along with technical expertise, they often assess behavioral skills to gauge how well you can collaborate in dynamic teams and manage real-world challenges. Understanding the types of questions they ask can give you a distinct advantage and boost your confidence.

In this guide, I’ve compiled a powerful set of AT&T Software Engineer Interview Questions that will prepare you for every stage of the hiring process. These questions are designed to help you tackle coding challenges, ace system design discussions, and respond to behavioral scenarios with ease. By exploring this content, you’ll gain the insights and tools needed to stand out as a strong candidate and make a lasting impression during your next interview. Let’s get started and ensure you’re fully prepared to take the next step in your career with AT&T!

1. What is the difference between a stack and a queue?

A stack is a data structure that follows the LIFO (Last In, First Out) principle, meaning the last element added is the first to be removed. I can think of a stack as a stack of plates—when I add a plate to the stack, it goes on top, and when I remove a plate, I take the one from the top first. In programming, we typically use a stack to store data temporarily while executing operations like function calls or undo operations in software.

On the other hand, a queue follows the FIFO (First In, First Out) principle. It works much like a line at a ticket counter: the first person to join the line is the first one to be served. In programming, queues are used in scenarios like scheduling tasks or managing requests in a server. I can imagine a queue being ideal for situations where I need to process data in the same order it was received.

2. Explain the concept of inheritance in object-oriented programming.

In object-oriented programming (OOP), inheritance is a mechanism where a new class (child class) inherits the properties and behaviors (methods) of an existing class (parent class). I can think of inheritance as a family tree. Just like how a child inherits traits from their parents, a child class can inherit attributes and methods from its parent class. This helps in reusing code, which makes the program more efficient and easier to maintain.

For example, in a class hierarchy, a parent class like Animal might have common attributes like name and methods like speak(). A child class, say Dog, could inherit these attributes and methods and even extend them with additional features, like a method specific to dogs, such as bark(). In this way, I don’t have to rewrite the basic functionality in every class, leading to a more modular and maintainable code structure.

3. How do you reverse a linked list in Python?

To reverse a linked list in Python, I would start by traversing through the list and rearranging the next pointers of each node. I would maintain three pointers: one for the current node, one for the previous node, and one for the next node. This allows me to reverse the direction of the list without losing track of the rest of the nodes.

Here’s a simple example of how I might reverse a linked list in Python:

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

def reverse_linked_list(head):
    prev = None
    current = head
    while current:
        next_node = current.next
        current.next = prev
        prev = current
        current = next_node
    return prev

In this code, I start by setting the prev pointer to None since the new head of the reversed list will point to None. The current pointer starts at the head of the list. For each node, I store the next node temporarily, reverse the next pointer of the current node, and move both pointers forward. The new head of the reversed list is returned once the entire list has been traversed.

4. What is the difference between an abstract class and an interface in Java?

In Java, both an abstract class and an interface are used to define a blueprint for other classes to implement, but there are key differences between the two. An abstract class can have both abstract methods (methods without implementation) and concrete methods (methods with implementation). This gives me the flexibility to define default behavior in an abstract class while also forcing subclasses to implement certain methods. An abstract class can also have fields and constructors.

On the other hand, an interface in Java can only have abstract methods (until Java 8, when default methods were introduced), and all methods in an interface are inherently public and abstract. Interfaces are generally used when I want to define a contract that multiple classes, even those from different inheritance hierarchies, can implement. I would use an interface to represent behaviors that are not dependent on the class hierarchy, whereas I would use an abstract class when I want to share some common code and enforce a certain structure in subclasses.

5. Describe the concept of polymorphism with an example.

Polymorphism in object-oriented programming allows me to use a single interface to represent different underlying forms (data types). It comes in two main types: compile-time polymorphism (also known as method overloading) and run-time polymorphism (also known as method overriding). In method overloading, multiple methods can have the same name but differ in the number or type of parameters. In method overriding, a subclass provides its own specific implementation of a method already defined in its parent class.

For example, consider the following code where method overriding is used:

class Animal {
    void sound() {
        System.out.println("Animal makes a sound");
    }
}

class Dog extends Animal {
    @Override
    void sound() {
        System.out.println("Dog barks");
    }
}

public class Main {
    public static void main(String[] args) {
        Animal myAnimal = new Animal();
        Animal myDog = new Dog();
        
        myAnimal.sound();  // Outputs: Animal makes a sound
        myDog.sound();     // Outputs: Dog barks
    }
}

In this example, the method sound() is overridden in the Dog class. Even though both myAnimal and myDog are of type Animal, the actual method called depends on the object type at runtime. This is run-time polymorphism. By using polymorphism, I can write more flexible and reusable code where I can treat objects of different types uniformly, improving maintainability and scalability.

6. What are the main advantages of using an array over a linked list?

One of the main advantages of using an array over a linked list is that arrays provide constant-time access to their elements. This is because array elements are stored in contiguous memory locations, and I can directly access an element at any index using the index value in O(1) time. This makes arrays very efficient when I need to access or update elements at specific positions. In contrast, a linked list requires linear time traversal (O(n)) to reach a specific element because each element is scattered in memory, and I need to follow the links (or pointers) from the head to the desired node. Arrays also offer better cache locality, meaning the CPU cache can load multiple elements at once, improving performance. However, arrays have fixed sizes, whereas linked lists can grow dynamically. If I need to frequently resize or insert/remove elements in the middle, a linked list might be a better choice.

7. Explain the use of the “this” keyword in Java.

In Java, the this keyword refers to the current instance of the class. It’s commonly used to distinguish between instance variables and local variables when they have the same name. For example, if I have a constructor with a parameter that has the same name as an instance variable, I can use this to refer to the instance variable. Here’s a quick example to demonstrate this:

class Person {
    String name;
    Person(String name) {
        this.name = name;  // 'this.name' refers to the instance variable, 'name' refers to the constructor parameter
    }
}

In the constructor, this.name refers to the instance variable, and name refers to the parameter. This distinction helps avoid confusion and ensures the correct variable is being modified. Additionally, I can use this to call other constructors in the same class, for example, in constructor chaining.

8. What is the time complexity of the binary search algorithm?

The binary search algorithm is a highly efficient method for finding an element in a sorted array. Its time complexity is O(log n), where n is the number of elements in the array. The reason binary search is so efficient is that it repeatedly divides the search interval in half. Initially, I compare the target element with the middle element of the array. If the target is equal to the middle element, the search is over. If the target is smaller, the search continues in the left half of the array; if it’s larger, the search continues in the right half. With each comparison, the size of the search space is halved, making the algorithm logarithmic in time complexity. This is much faster than linear search, which has a time complexity of O(n).

9. Can you explain the difference between a deep copy and a shallow copy in Python?

In Python, shallow copy and deep copy refer to two types of copying mechanisms, and they differ in how they handle the objects inside a container. A shallow copy creates a new container object but doesn’t create copies of the objects inside the container. Instead, it copies references to the original objects. This means that changes to the inner objects of a shallow copy will also affect the original container. I can create a shallow copy using the copy() method or the copy module in Python. Here’s an example of a shallow copy:

import copy
original = [[1, 2], [3, 4]]
shallow = copy.copy(original)
shallow[0][0] = 99
print(original)  # Output: [[99, 2], [3, 4]]

A deep copy, on the other hand, creates a completely new object and also recursively copies all objects inside it. This means that changes made to the deep copy do not affect the original object. I can create a deep copy using the deepcopy() function from the copy module. Example of a deep copy:

import copy
original = [[1, 2], [3, 4]]
deep = copy.deepcopy(original)
deep[0][0] = 99
print(original)  # Output: [[1, 2], [3, 4]]

10. What is a hash map and how does it work?

A hash map (also known as a dictionary in Python or a hash table) is a data structure that stores key-value pairs. The main advantage of a hash map is that it provides fast lookups, insertions, and deletions in O(1) time on average. It works by using a hash function to compute an index (or hash) where the corresponding value is stored. In simple terms, when I want to add an element, I apply a hash function to the key, which determines the index at which the value will be stored. To retrieve the value, I apply the same hash function to the key and access the value at that index. If two keys happen to hash to the same index (called a collision), the hash map typically uses techniques like chaining (using a linked list) or open addressing to resolve the collision.

Here’s a simple example in Python:

my_map = {}
my_map["apple"] = 10
my_map["banana"] = 20
print(my_map["apple"])  # Output: 10

In this example, I create a hash map, add two key-value pairs, and then access the value associated with the key "apple". Hash maps offer a constant-time lookup, which makes them very efficient.

11. What is the difference between an exception and an error in programming?

In programming, the terms exception and error both refer to issues that occur during the execution of a program, but they are treated differently. An exception is an event that disrupts the normal flow of a program but can be handled with exception handling mechanisms like try-catch in Java or try-except in Python. Exceptions usually represent expected issues that a program can anticipate, like dividing by zero or trying to open a file that doesn’t exist. An error, on the other hand, typically refers to a more serious issue, such as an out of memory error or a system crash, which usually cannot be recovered from within the program. Errors are often represented as system-level problems, whereas exceptions are typically recoverable problems. In Python, for instance, exceptions are instances of the Exception class, while errors are instances of the Error class (like MemoryError).

12. How would you implement a basic queue using two stacks?

To implement a queue using two stacks, I can use the enqueue stack to push elements when adding to the queue, and the dequeue stack to pop elements when removing from the queue. The trick is to only move elements between the stacks when the dequeue operation is called. If the dequeue stack is empty, I transfer all the elements from the enqueue stack to the dequeue stack, reversing the order of the elements, making the oldest element on top. Here’s a simple implementation in Python:

class QueueUsingStacks:
    def __init__(self):
        self.stack1 = []
        self.stack2 = []

    def enqueue(self, item):
        self.stack1.append(item)

    def dequeue(self):
        if not self.stack2:
            while self.stack1:
                self.stack2.append(self.stack1.pop())
        if self.stack2:
            return self.stack2.pop()
        return None  # Queue is empty

queue = QueueUsingStacks()
queue.enqueue(1)
queue.enqueue(2)
print(queue.dequeue())  # Output: 1

In this implementation, when dequeue() is called, I check if stack2 is empty. If it is, I transfer all elements from stack1 to stack2, which reverses their order. This ensures that the first element inserted is the first to be removed, mimicking the behavior of a queue.

13. What is a deadlock in multithreading, and how can it be avoided?

A deadlock in multithreading occurs when two or more threads are blocked forever because they are each waiting for the other to release a resource. This creates a circular dependency where no thread can proceed. For example, thread A might hold lock 1 and wait for lock 2, while thread B holds lock 2 and waits for lock 1, causing both threads to wait indefinitely. To avoid deadlock, I can use several strategies: – Lock ordering: Ensure that all threads acquire locks in the same order to avoid circular dependencies. – Timeouts: Implement a timeout when acquiring locks, so that if a thread cannot acquire all necessary locks within a certain time, it releases any acquired locks and tries again. – Deadlock detection: Periodically check for circular dependencies in the lock system and break them when detected.

14. What are the main features of RESTful APIs?

A RESTful API (Representational State Transfer) is an architectural style for designing networked applications. The main features of a RESTful API are: – Statelessness: Each request from a client to a server must contain all the information needed to understand and process the request. The server does not store any state between requests. – Uniform Interface: A RESTful API uses a consistent and standard set of HTTP methods, such as GET, POST, PUT, DELETE, to perform actions on resources. – Resources: Resources (such as data objects) are represented by URLs, and these resources can be manipulated using standard HTTP methods. – Scalability: RESTful APIs are designed to handle large-scale applications by using stateless communication and leveraging HTTP caching mechanisms.

15. Explain the concept of memory management in Java.

Memory management in Java is handled by the Java Virtual Machine (JVM), which automatically manages the memory used by the program. Java uses automatic garbage collection to reclaim memory that is no longer in use. When an object is no longer referenced, it becomes eligible for garbage collection, which frees up memory for future use. Java’s memory is divided into two main regions: the heap and the stack. The heap is used for dynamic memory allocation, where objects are stored. The stack is used for method calls and local variables. The JVM takes care of allocating and deallocating memory, and developers don’t need to manually manage memory like in languages such as C or C++. However, understanding memory management helps in optimizing performance by avoiding memory leaks and excessive garbage collection.

16. How would you design a URL shortening service like bit.ly?

In designing a URL shortening service, the primary goal is to take a long URL and convert it into a short, unique identifier, making it easier to share. One way I would approach this is by creating a simple system where the user submits a long URL, and the service generates a unique shortened URL. I would store the mapping between the long URL and its corresponding short URL in a database. Each time a short URL is accessed, the service looks up the long URL in the database and redirects the user to it. A key component here is generating unique short URL identifiers. I could use a base-62 encoding scheme (using characters a-z, A-Z, and 0-9) to create short and human-readable URLs. Here’s a basic implementation of how to generate a short URL:

import random
import string

def generate_short_url(length=6):
    characters = string.ascii_letters + string.digits
    short_url = ''.join(random.choice(characters) for i in range(length))
    return short_url

print(generate_short_url())

In this snippet, I create a random short URL consisting of alphanumeric characters. This generates a new URL every time it’s called. To store the mapping between long and short URLs, I would typically use a hash table or a database. For real-world systems, I’d ensure uniqueness by checking if the short URL already exists before generating a new one.

17. Describe how you would optimize a database query to improve performance.

In my experience, optimizing a database query involves several strategies to reduce the time it takes to retrieve data. One common approach is to ensure that indexes are created on frequently queried columns, especially on primary keys and foreign keys. For example, if I have a database table with user information and I frequently query based on the user’s email, creating an index on the email column can drastically improve performance. Another strategy I would consider is avoiding **SELECT *** in queries. Instead, I would select only the necessary columns, which reduces the amount of data transferred from the database. Additionally, if I’m working with large datasets, I’d implement pagination in queries to load a subset of data at a time.

Here’s an example of how I would optimize a SQL query:

SELECT user_id, user_name FROM users WHERE email = 'example@example.com';

In this query, I’m only selecting the user_id and user_name columns, which are required, instead of selecting all columns. Additionally, I could add an index to the email column to make the query faster. I’d also consider optimizing joins by ensuring that they are done on indexed columns and by using proper query execution plans to analyze and improve query performance.

18. How would you implement a recommendation system for an e-commerce website?

In my experience, implementing a recommendation system for an e-commerce website can be done using a variety of techniques. A popular approach is collaborative filtering, where I use data from other users who have similar preferences to recommend products. This can be done by finding patterns in user behavior, such as items that are frequently bought together. Alternatively, I could use content-based filtering, where recommendations are based on the features of products that a user has already interacted with. For example, if a user bought a specific brand of shoes, I could recommend other shoes from the same brand or with similar attributes like size or color.

Here’s a basic implementation using Python’s pandas library:

import pandas as pd
import numpy as np

# Sample data: users and products they've interacted with
data = {'user': ['A', 'B', 'C', 'D'], 'product': ['Shoes', 'Shirt', 'Shoes', 'Hat']}
df = pd.DataFrame(data)

# Create a user-product interaction matrix
matrix = pd.pivot_table(df, index='user', columns='product', aggfunc=len, fill_value=0)
print(matrix)

In this example, I create a user-product interaction matrix, which shows which users interacted with which products. I can then apply collaborative filtering algorithms such as user-based or item-based to recommend products to users based on similar preferences.

19. How would you design a scalable system for real-time messaging?

When designing a scalable system for real-time messaging, one of the first things I would focus on is ensuring the system can handle many simultaneous users without becoming overloaded. I would use a message queue like RabbitMQ or Kafka to handle the message delivery. These systems allow me to decouple the producer (sender) and consumer (receiver) of the messages, enabling better scaling. For message delivery, I would use WebSockets to establish real-time, bidirectional communication between the client and server. This allows messages to be sent and received instantly, without the need for repeated HTTP requests.

Here’s a simple example of a WebSocket server using Python’s websockets library:

import asyncio
import websockets

async def echo(websocket, path):
    message = await websocket.recv()
    await websocket.send(f"Received: {message}")

start_server = websockets.serve(echo, "localhost", 8765)

asyncio.get_event_loop().run_until_complete(start_server)
asyncio.get_event_loop().run_forever()

In this example, the server listens for messages and sends a response back to the client in real-time. By using a message queue and WebSockets, I can ensure that messages are delivered quickly and efficiently, even when the number of users scales up.

20. What is the purpose of sharding in databases, and how does it improve scalability?

The purpose of sharding in databases is to divide the database into smaller, more manageable parts called shards. This is particularly useful for scaling horizontally, as it allows the system to distribute data across multiple servers. Sharding helps improve performance and availability by reducing the load on a single server and spreading the data across multiple machines. Each shard contains a subset of the data, which means queries can be parallelized and executed more quickly. Sharding is typically done based on a key, such as user ID or geographic location, ensuring that related data is stored together in the same shard.

Here’s an example of how data might be distributed across multiple shards:

-- Shard 1 stores data for users with IDs 1 to 1000
-- Shard 2 stores data for users with IDs 1001 to 2000

In this example, I have two shards, each handling different subsets of the user data. If the system grows and the load increases, I can add more shards to distribute the data further. This ensures that the database remains scalable and can handle increased traffic. By distributing data across multiple servers, sharding significantly improves the performance and availability of the database.

21. Describe a situation where you had to troubleshoot a performance issue in a web application. How did you approach it?

In one of my previous projects, we had a web application experiencing slow page loads, especially on high-traffic pages. To troubleshoot the issue, I first used browser developer tools to analyze the network performance and identified that the page load time was affected by large, unoptimized images. I also found that certain JavaScript files were blocking the rendering of the page. After pinpointing these bottlenecks, I optimized the images by compressing them and implemented lazy loading for images, ensuring that they only loaded when they came into view. Additionally, I used asynchronous loading for non-essential JavaScript files, so the page content could load faster. Here’s an example of lazy loading in JavaScript:

// Lazy loading an image
let images = document.querySelectorAll('img.lazy');
images.forEach((image) => {
    image.src = image.dataset.src; // Set the actual image source
    image.classList.remove('lazy'); // Remove lazy class after loading
});

To further investigate the performance issue, I also checked the server-side performance using application performance monitoring (APM) tools like New Relic. This allowed me to pinpoint slow database queries and reduce the load on the server by optimizing those queries and adding proper indexing. The combination of both frontend and backend optimizations resulted in a significant improvement in the page load speed, reducing it by over 50%. In my experience, a methodical approach combining both frontend optimization and backend improvements is the key to resolving performance issues in web applications.

22. You are working on a project with a tight deadline. A bug is reported in the system, but it doesn’t seem to be critical. How do you prioritize fixing it while managing time constraints?

When I encounter a non-critical bug under tight deadlines, I prioritize the tasks by assessing the bug’s impact on the overall functionality. First, I categorize the bug based on its severity and determine if it will affect the core functionality or is just a minor annoyance. In cases where the bug does not impact the user experience significantly or the system’s core features, I would document the bug and defer it to be addressed after the more urgent tasks are completed. I would also communicate with the team or project manager about the bug’s priority and set expectations about the timeline for fixing it.

In my experience, time management is key when handling multiple priorities. I would allocate time for the bug fix only if it can be addressed quickly without diverting too much attention from more critical tasks. If the bug could be fixed in less than an hour and won’t significantly delay the project, I would consider addressing it immediately. Otherwise, I would ensure it is tracked and scheduled for a later time after delivering the more pressing features or tasks. Properly balancing critical tasks with minor issues ensures that tight deadlines are met without compromising the quality of the project.

23. Imagine you’re part of a team that has to integrate a legacy system with a new application. How would you ensure smooth integration and minimize potential issues?

When integrating a legacy system with a new application, one of the first steps I would take is to thoroughly understand the existing architecture and identify any dependencies or limitations that could affect the integration. In my experience, legacy systems often come with outdated technologies and complex workflows that may not be compatible with modern systems. I would start by creating a detailed integration plan that outlines the data flow between the systems, defines the required interfaces, and sets expectations for any required refactoring on the legacy side. Communication with stakeholders is crucial here to ensure all requirements are understood and aligned.

To ensure smooth integration, I would focus on incremental integration rather than attempting to integrate everything at once. This allows the team to isolate potential issues and address them step by step. I would also implement automated tests to ensure that any changes made during the integration process do not break existing functionality. In the event of data incompatibilities, I would suggest using middleware to bridge the gap between the old and new systems, allowing for seamless communication. By following these steps and maintaining constant communication, I can ensure that the integration goes smoothly and with minimal issues.

24. You’re working with a team of developers, and there is disagreement over the architecture of a system. How would you handle this situation to reach a consensus?

In situations where there’s a disagreement over system architecture, I believe it’s important to first ensure that everyone has a clear understanding of the project’s goals and requirements. I would organize a meeting where each team member can present their perspective, supported by reasons or past experiences that justify their approach. I find that focusing on the project’s objectives—such as performance, scalability, maintainability, and time-to-market—can help steer the conversation toward what’s best for the project. I would encourage an open dialogue, ensuring all viewpoints are heard, and use data or examples to support the discussion.

Once all options are on the table, I would suggest performing a trade-off analysis, weighing the pros and cons of each approach in terms of how it meets the project goals. If necessary, I would prototype a few concepts or create a small-scale proof of concept (POC) to demonstrate which architecture performs better in real-world conditions. Ultimately, the goal is to reach a consensus that benefits the project as a whole. In my experience, addressing disagreements collaboratively and focusing on the long-term benefits of each option helps build team unity and ensures the best choice is made.

25. Suppose you have multiple microservices in a large-scale application, and one of them is experiencing high latency. How would you go about identifying and resolving the issue?

When dealing with high latency in one of the microservices, I would first examine the microservice’s logs and use distributed tracing tools like Jaeger or Zipkin to trace the requests and identify where the delay is occurring. These tools allow me to visualize the flow of requests across microservices and pinpoint where the bottleneck is. Additionally, I would monitor resource utilization (CPU, memory, disk I/O) to determine if the service is resource-starved, and I would use tools like Prometheus to collect metrics on performance.

Once I have identified the root cause, such as a slow database query or an inefficient algorithm, I would work on optimizing that specific service. If the issue is with a database query, for example, I would review the query and ensure proper indexing and optimization. In some cases, I might introduce caching to reduce the number of calls to the database or external services, improving response times. Here’s an example of caching a database result with Redis in Python:

import redis
import json

# Connect to Redis
r = redis.StrictRedis(host='localhost', port=6379, db=0)

def get_data_from_db(query):
    # Check if data is already cached
    cached_data = r.get(query)
    if cached_data:
        return json.loads(cached_data)
    else:
        # Simulate a database call
        data = simulate_db_query(query)
        # Cache the data for future use
        r.set(query, json.dumps(data))
        return data

def simulate_db_query(query):
    # Simulate a database query and response
    return {"result": "data for " + query}

By continuously monitoring the microservice’s performance after implementing fixes, I can ensure that latency issues are resolved and prevent them from reoccurring in the future.

Conclusion

Success in AT&T’s Software Engineer interview lies in your ability to tackle both fundamental and advanced technical challenges with confidence. Beyond just answering questions, the interview process is designed to assess your problem-solving mindset, technical depth, and how well you can navigate real-world complexities. Demonstrating your knowledge in data structures, algorithms, system design, and coding will show that you’re not just a good fit for the role but someone who can thrive in AT&T’s dynamic tech environment.

What sets you apart is not just your technical skills but also your approach to collaboration, communication, and continuous learning. AT&T seeks engineers who can adapt to new challenges, innovate solutions, and work seamlessly with cross-functional teams. As you prepare, focus on sharpening both your technical expertise and your ability to communicate complex ideas effectively. With focused preparation and the right mindset, you’ll be ready to make a lasting impression and set yourself on the path to success in AT&T’s competitive hiring process.

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