Software Architect Interview Questions and Answers for 2025

Shared nothing architecture (SNA) is an approach to distributed computing that does not involve the sharing of any physical resources between nodes. Instead, each node has its own memory, storage, and processing power so that no other node needs to access them.  

This architecture allows for more scalability than traditional systems because each node can independently process requests without having to wait for another node to finish first. Additionally, SNA eliminates the need for synchronization between nodes, which reduces latency in communication between nodes and improves overall system performance.  

The scalability of shared nothing architecture depends on the number of nodes in the system and how they are configured. The more nodes there are, the more scalability potential there is since each node can handle its own requests without waiting for other nodes to catch up.  

Additionally, if the nodes are organized into tiers—with some nodes handling more intensive tasks than others—then those tasks can be spread out across multiple tiers for improved performance and scalability. Finally, SNA also makes it easier to add or remove nodes from the system when necessary as long as all of the existing data is properly partitioned across the different nodes beforehand.  

YAGNI stands for “You Ain’t Gonna Need It.” YAGNI is a principle that was first introduced in Extreme Programming (XP). It encourages software developers to focus on the current requirements of a project and not code for future possibilities that may never be needed. This approach helps to prevent over-engineering and excessive complexity when developing software applications. In other words, it is about writing code only when there is a need for it now or in the near future – not speculatively coding for potential needs down the line.  

Practicing YAGNI means being mindful about which features and functions you include in your application design. For example, if you don’t have a clear use case for a feature then it’s probably best not to include it until you actually need it later down the line. This way, you won’t be wasting valuable resources on features that may never get used anyway. 

Similarly, if you have identified a feature that could potentially be useful but there isn’t an immediate need right now then put off coding until there is one – this way your application remains lean and manageable while still fulfilling its purpose effectively. 

YAGNI allows developers to avoid creating unnecessary code or features that will not be used by users or customers. YAGNI also helps to keep development costs low as developers do not have to spend time developing features that may never be used.

KISS helps developers strive to create easy-to-understand solutions with minimal complexity. This means that developers should focus on making sure their code is well-structured and organized so that users can easily understand how it works without having any technical knowledge about coding.

The main difference between these two principles lies in their primary focus – while KISS emphasizes keeping things simple, YAGNI emphasizes avoiding unnecessary work. While both are important considerations when building software applications, their end goals differ slightly – KISS focuses on making sure existing features remain as easy-to-use as possible while YAGNI focuses on making sure new features don’t become a burden before they're even necessary. Both are essential aspects of successful software architecture that should be taken into account when designing new applications or updating existing ones.  

Heuristic expressions are mathematical formulas that use specific rules of thumb which provide a quick solution to a problem. They are often used when an exact answer isn’t available or when there’s not enough data to make an accurate calculation. This allows the software architect to quickly come up with approximate solutions that may still be useful while avoiding spending too much time on complex calculations.

Heuristics can be used in virtually any area of software engineering, but they have become especially popular in testing and debugging. When developers are trying to debug a program, they may not have time or resources to carefully trace every line of code and identify the source of errors — instead, they might use heuristics to quickly pinpoint potential sources of errors. For example, if they notice that certain pieces of code seem to cause more errors than others, then they can apply heuristics such as “code X is likely causing Y error” or “code X is likely related to Y issue”. This helps them narrow down the number of lines of code that need further investigation and save precious time in the process.

Heuristics can also be used by architects when designing architectures for their applications. By using heuristics such as "this component will likely have high traffic" or "this component will likely require more storage", architects can quickly determine which components should get priority during design and implementation phases, saving them time and energy while still ensuring that their application performs optimally.   

Amdahl's law illustrates the tradeoff between speed and scalability when designing a system. It states that the overall speed of a system is limited by its slowest component — no matter how much faster the other components may be. To use an analogy, think of a relay race where each runner must run at their own pace; no matter how fast the other team members run, if one runner goes slow then the team will still lose. The same holds true for systems; if one component is not running optimally then it will limit the overall performance of the entire system.

Amdahl's law can be used to calculate the maximum theoretical improvement in speed that can be achieved by upgrading certain parts of a system. For example, if you are developing a software application and want to improve its performance, Amdahl's law can help you decide which components should be upgraded first in order to achieve the best result. By understanding this principle, software architects can make informed decisions about their designs that will have long-term effects on their project's success.

A GOD class (or God object) is a computer programming concept used to describe classes that are overly large or too tightly coupled. It usually refers to classes that contain too many methods or properties—often more than 1000 lines of code or 10 different methods—which makes them unwieldy and difficult to maintain. A single change in such a class can cause unexpected side effects because the entire system depends on it. As such, developers often refer to these classes as “GOD classes” since they seem omnipresent and difficult to control.

The biggest problem with GOD classes is that they tend to make the code rigid and hard to maintain. If you have one big class that does everything, then changing one part of it affects the whole system. This can lead to bugs and other issues when changes are made down the line. Additionally, tight coupling between components leads to bad design decisions which can be hard to refactor later on in development.

Furthermore, having large classes also makes debugging more difficult since there's more code to go through when trying to find the source of an issue. And if your application relies on multiple instances of these God objects throughout its structure, tracking down issues becomes even harder since they could be caused by any number of things across multiple locations in your codebase.  

IP address affinity is a technique used for load balancing in which requests from an individual IP address are always sent to the same web server. This ensures that users always get the same page no matter how many requests they make. This can be beneficial in certain scenarios, such as when personalization or session state information needs to be maintained between multiple sessions with the same user. By directing all traffic from an individual user to a single server, this information can be stored and easily accessed by that server on subsequent visits.  

The way IP address affinity works is relatively straightforward. When a request comes in, the load balancer checks the requesting IP address against its list of known addresses and then directs the request accordingly.  

If the requested IP address isn't already associated with any particular server, then the load balancer will select one randomly and assign it as that user's “affinity” server going forward, ensuring that their next visit will be sent directly to that server without further checking required. In addition, if an affinity server becomes unavailable or overwhelmed with traffic, then the load balancer can transparently reassign that user's requests to another available server without them having to do anything differently. 

The DRY principle states that “every piece of knowledge must have a single, unambiguous, authoritative representation within a system.” This concept is based on the idea that duplication can lead to confusion and errors in software development. By following the DRY principle, developers can prevent writing unnecessary code that could lead to problems down the line.

The DIE principle was created as an extension of the DRY principle. It states that code duplication should be avoided whenever possible, as it can lead to inconsistencies between different parts of the system or between multiple versions of the same codebase. Duplication also increases maintenance costs and can make debugging more difficult because there may be multiple places where changes need to be made in order to fix a bug or update a feature.

By following both the DRY and DIE principles, developers can create more robust and reliable software architectures that are easier to maintain over time. For example, if a developer is creating a web application with multiple features that require similar functionality, they should use abstraction techniques like functions or classes instead of repeating code across different parts of the application. This ensures that any changes made in one part of the system will automatically be reflected in other parts as well, reducing bugs and speeding up development time.

The Single Responsibility Principle (SRP) is a principle of object-oriented programming that states that each module or class in a program should have only one responsibility. This means that each module or class should be responsible for only one type of functionality, such as data access, input/output handling, or business logic. The idea behind this principle is that it makes code more organized and easier to maintain by breaking down large programs into smaller modules or classes with specific responsibilities.

The Single Responsibility Principle is an important concept because it makes code easier to read and understand. If a module has multiple responsibilities, it can become difficult to keep track of all the different parts and how they interact with each other. By limiting each module to just one responsibility, it makes reading and understanding code much easier. Additionally, if changes are needed in one area of the program, then it can be done without affecting other areas of the program since the modules are isolated from each other. This makes maintenance much simpler since any changes made won't affect any other part of the system.

In addition to making code easier to read and maintain, SRP also helps improve overall system performance by reducing complexity and improving scalability. By keeping modules focused on a single purpose instead of having them handle multiple tasks at once, you can ensure that system resources are used efficiently and not wasted on unnecessary tasks. This increases performance by ensuring your application runs faster and more reliably than if all tasks were handled by one large module with multiple responsibilities. 

The twelve-factor app is a set of best practices created by Heroku in 2011 to help software teams better manage their applications. These principles provide guidance on how to design and deploy applications that can scale quickly and reliably. The twelve factors include:  

1. Codebase: The codebase should be version controlled so that it can easily be tracked and maintained.  
2. Dependencies: All application dependencies should be explicitly declared in the configuration files instead of relying on implicit dependencies from external sources.  
3. Config: All configuration should be stored in environment variables instead of hardcoding them into the application codebase.  
4. Backing Services: Applications should rely on external backing services like databases or third-party APIs instead of storing data locally within the application itself.  
5. Build, Release and Run: Applications should use a specific build process for creating deployable artifacts that can then be released into production environments. These artifacts should also include scripts for running the application in production environments.  
6. Processes: Applications should run as one or more processes (e.g., web server, database server). These processes should run independently of each other and not share any resources between them.  
7. Port Binding: Applications should bind themselves to a port so that they can accept requests from outside sources without requiring any manual steps or configuration changes.  
8. Concurrency: Applications should be designed to handle multiple tasks concurrently by using multiple processes or threads within each process where appropriate.  
9. Disposability: Applications should start up quickly and terminate gracefully when necessary so that they can easily be stopped/started during maintenance operations or if there is an unexpected failure in the system.  
10. Dev/Prod Parity: Development environments should closely match production environments so that issues discovered during testing can be quickly reproduced in production environments if necessary.  
11. Logs : Applications must log all output from their processes in order to diagnose issues quickly during development or debugging operations  
12. Admin Processes : Administrative tasks such as database migrations or backups must also run as separate processes from the main application(s).  

Fault tolerance and fault resilience are both strategies for dealing with system errors. Fault tolerance is the process of anticipating errors in a system and making sure that those errors don't cause significant damage to the system as a whole. This type of error handling is focused on prevention, meaning that it relies on careful planning to make sure that no single component of the system can cause catastrophic failure. It's important to note that while fault tolerance is an effective strategy, it can be costly in terms of time and resources.

Fault resilience, on the other hand, is focused more on recovery than prevention. When an error occurs in a resilient system, it will be able to recover quickly without any major disruption to its operations. While this type of error handling may not completely prevent errors from occurring, it will help ensure that any downtime due to errors or failures is minimal. As such, it can be a cost-effective way of ensuring continuity despite occasional issues. 

CQRS is an architectural pattern that separates read operations from write operations. In other words, it allows for separate optimization of writing data into a database and reading data from a database.

Event sourcing is an architectural pattern that ensures every change to an application's state is recorded as an event. The events are stored in a log or sequence of records that can be used to reconstruct an application's state at any given time.

Yes! It is possible to use CQRS without event sourcing; however, there are certain advantages that come with combining these two patterns together. For example, when both CQRS and event sourcing are employed together, developers have more control over which parts of the system need to be updated when making changes—which can help reduce complexity and improve performance by reducing redundant work. Additionally, using both patterns together makes it easier for developers to audit changes made over time by providing visibility into each step of the process.  

The basic idea behind isolating domain entities from the presentation layer is that these two components are responsible for different tasks. The domain entity is responsible for handling data and logic related to a specific application or system, while the presentation layer handles user interaction with the system. By keeping these two elements separate, you can ensure that each component performs its intended function without interference from one another. This helps make the software more reliable and efficient.

Additionally, separating out domain entities from the presentation layer allows you to make changes more easily without affecting other parts of the system. For example, if you need to add a new feature or change an existing one in the application, you can do so without having to alter anything in the presentation layer. This makes updating and maintaining your software much simpler and less time consuming.

Moreover, having an isolated domain entity also allows for better scalability as well as improved security of your application or system. With a separate domain entity in place, it becomes easier to scale up or down based on demand, as well as protecting against potential cyber-attacks by keeping sensitive information away from malicious actors. 

The Command and Query Responsibility Segregation (CQRS) pattern is an architectural pattern used for separating read operations from write operations within an application or system. The goal of this separation is to improve scalability by allowing both sets of operations to scale independently of each other. Furthermore, CQRS also improves flexibility by allowing for different approaches in implementing each set of operations as well as reliability by reducing latency in query responses due to the separate data stores being used for each type of operation.

The core idea behind CQRS is that when a user interacts with an application or system, they will either be issuing a command or making a query. Commands are typically issued to modify data while queries are issued to retrieve data. By separating these two types of operations into two different models, each model can be optimized independently for its own specific purpose. This can lead to improved performance since querying and writing can take place in two separate databases which can be tuned and scaled independently from one another.

By separating commands from queries, applications are able to better utilize resources such as memory and processing power since they won’t have to run both types of operations simultaneously on the same hardware or platform. 

Additionally, using separate models for commands and queries allows developers more flexibility when it comes time to make changes as they can update one model without affecting the other. Finally, using CQRS makes applications more reliable since queries will not have any impact on commands which means that query responses will not be affected by any delays caused by writing data.   

Sharding is the process of dividing up a large database into multiple smaller databases, called shards, each of which contain only a fraction of the data contained in the original database. These shards are then stored on separate servers so that they can be accessed independently. The goal of sharding is to increase performance by reducing the amount of data that needs to be processed when accessing or updating information in the database. It also allows for scalability since more shards can be added as needed without having to scale up the entire server infrastructure.

Sharding is an important technique because it can help improve overall performance and scalability by reducing contention on resources. When data is stored in multiple shards, there are fewer requests competing for resources at any given time, which means that queries can be processed faster and more efficiently.

Furthermore, adding additional shards as your datasets grow will help minimize downtime due to high load on servers and allow you to scale quickly as your business needs grow. Finally, sharding also makes it easier to maintain consistency across databases since each shard contains only a portion of the data from the original dataset. 

The Repository Pattern is an abstraction of data access that functions as a collection to provide in-memory data operations. It's particularly useful for implementing changes on multiple objects, like within databases. The Active Record Pattern, on the other hand, will use an object-relational mapping system for storing data in tables, using ORM-specific models for querying and persisting the data.

At their core, both patterns are designed to provide access to related pieces of information from databases without requiring code changes when the database structure is modified; however, they do so in distinct ways. For example, the Repository Pattern makes no assumption about properties of classes or class relationships while the Active Record Pattern relies on object-oriented principles such as static typing and inheritance that make assumptions about classes and their relationships that must be met for it to work correctly.

An actor model in the context of a programming language is an asynchronous process-oriented programming style. In this model, data and instructions are represented as “actors” which contain their own state, behavior and information about communication with other actors. Each actor can send messages and change its internal behavior in response to the messages it receives.

Central to this architecture is a runtime system called an Actor Supervisor which offers scheduling, resource management and communication services that enable these programs to run efficiently on distributed systems like cloud applications and parallel architectures. The goal of using an actor model is to create applications that are resilient, independent from environment settings, easily scalable and distributed without major effort. 

The Event Sourcing Pattern is a technique used in software architecture that enables the ability to save changes to an application's state as a sequence of events. The Pattern is built on top of the Command Query Responsibility Segregation (CQRS) principle, which dictates that application functions for writing data (commands) should be separate from those for reading data (queries).

Event Sourcing further expands upon CQRS by taking these ‘write’ operations one step further and storing them as a log or record list of all events following each other in succession. This provides an immutable record of any prior state and facilitates recreating instance lags or replaying sequences of past events.

Essentially, the Event Sourcing Pattern allows developers to take advantage of even more granular control over how their applications are modified and operated. Thus, it has become an invaluable difference-maker when creating architecture solutions that need to run at maximum efficiency without sacrificing accuracy or reliability. 

The “fail fast” approach is just what it sounds like – it encourages us to anticipate potential problems and design our system accordingly so that it fails quickly in the event of an error or unexpected circumstances. This means that any issues are caught before they become more serious and costly to fix. The main benefit of this strategy is that it reduces our risk by ensuring that any errors are identified as soon as they occur, reducing our exposure time with potentially disastrous consequences. The downside is that this approach can be quite costly if not implemented properly, as all potential points of failure must be anticipated beforehand.

On the other hand, the “robust” approach focuses on making sure that our system will continue functioning even when faced with unexpected conditions or events. This means that instead of anticipating every potential issue, we build systems and components to withstand any kind of problem or issue without completely failing. This strategy has its own set of benefits, such as being able to better handle unexpected scenarios without having to anticipate them ahead of time (which is impossible in many cases).

However, this method can also be very costly if not implemented carefully; while robustness can prevent catastrophic failures, it can also lead to sluggish performance or increased complexity which may drive users away from your product or service. 

Unit tests focus on individual pieces of code while integration tests assess how different components interact with each other; smoke tests detect major issues before moving onto further depth testing; and regression tests make sure no changes have broken existing functionality within your applications' codebase.

• Unit Test

A Unit Test is a type of software testing that focuses on individual units or components of code and verifies their correctness. The purpose of a unit test is to validate that each unit of the software performs as designed. This type of test isolates a section of code and determines if its output is correct under certain conditions. These tests are usually conducted using automated tools like JUnit, NUnit, and Jasmine.

• Integration Test

Integration Tests are used to verify the functionality between different units or components within the system being tested. They are designed to evaluate how well different parts of an application work together as a whole. Unlike Unit Tests which focus on individual components, Integration Tests focus on multiple components interacting with each other in order to verify that all elements are working properly together. These tests can be performed manually or through automation tools like Selenium WebDriver and Cucumber.

• Smoke Test

A Smoke Test is a type of testing used to identify any major problems with an application before performing further in-depth testing. It is often referred to as "Build Verification Testing" because its primary purpose is to determine if a build (stable version) is ready for further testing or not. This type of testing involves running basic functional tests against the application in order to ensure that its most critical functions are working correctly before moving forward with more detailed testing procedures such as regression tests and integration tests.

• Regression Test

Regression Testing is used to verify that changes made have not broken existing functionality in an application's codebase. It helps ensure that new updates have not caused any unwanted side effects in areas where they were not intended or expected. Regression Tests can be either manual or automated depending on the complexity and size of the project being tested, but they are typically done using some sort of automation tool such as Selenium WebDriver or Robot Framework Suite.