Top 29 Back End Developer Interview Questions and Answers [Updated 2025]

I have experience with Django and Flask. Django is great for rapid development because of its built-in features, but it can be heavy for simple projects. Flask, on the other hand, is lightweight and flexible, making it easy to use for microservices, but you have to implement a lot of features manually.

Object-Relational Mapping, or ORM, is a programming technique that allows developers to interact with a relational database using an object-oriented programming paradigm. It helps to simplify database interactions by mapping database tables to classes and database records to objects. ORM is appropriate when you want to speed up development and reduce boilerplate code, especially in applications with complex data relationships.

First, I would analyze the query execution plan to see where the slow parts of the query are, such as full table scans that can be avoided. Then, I would add appropriate indexes on the columns used in WHERE clauses or JOINs to speed up lookups.

A RESTful API is an application interface that uses HTTP requests to access and modify data, following principles like statelessness and resource-oriented architecture. In contrast, a SOAP API is built on XML and adheres to strict protocols and standards, making it heavier and more complex. REST is more flexible and uses formats like JSON which are easier to work with.

Synchronous programming executes tasks one after another, which can lead to blocking, whereas asynchronous programming allows tasks to run concurrently, improving efficiency. For example, in a web server, asynchronous processing can handle multiple requests without waiting for each to complete, enhancing user experience.

To design a database schema for e-commerce, I would create tables for Users, Products, Orders, and Categories. I'd normalize these tables but consider denormalizing some for performance. Indexing on Product IDs and Order dates would help speed up queries, and I would look into sharding for better scalability as traffic grows.

Common vulnerabilities include SQL injection, which can be mitigated by using parameterized queries. For XSS, I would ensure proper escaping of user inputs. Implementing CSRF tokens can protect against CSRF attacks, and regular security audits help identify potential vulnerabilities.

Microservices architecture is a way to design software applications as a collection of small, independently deployable services. Each service handles a specific function and communicates with others through APIs. This differs from monolithic architecture where the application is a single unit, making it harder to scale and deploy.

Using cloud services like AWS allows for easy scalability, meaning we can adjust resources based on demand. A challenge is potential vendor lock-in, which can complicate migration to other providers.

I manage code versions in Git by creating a new branch for each feature or bug fix. When I have completed work on a branch, I use 'git merge' to integrate changes into the main branch, preserving the history of both branches. Rebase is often used when I want to apply my changes on top of the latest commits from the main branch, creating a cleaner linear history.

I use JUnit for Java applications and ensure that my unit tests cover at least 80% of the codebase. I practice TDD by writing tests before coding the actual functionality, which helps in designing better APIs. Additionally, I leverage CI tools like Jenkins to automate running tests on each commit, ensuring that any regression is caught early.

Docker provides consistency across different environments, which makes deployments predictable. It allows us to scale services easily and ensures that dependencies are isolated. A challenge I faced was a performance issue with a database container, which I resolved by optimizing the container configuration and resource allocation.

I commonly use hash tables for quick lookups in user sessions due to their O(1) average time complexity for access, while I use arrays for situations where I need ordered data and efficient iteration.

First, I would review the update's changes and schedule the deployment during off-peak hours. I would run tests in a staging environment to ensure stability. Using a blue-green deployment, I would switch traffic to the new version while monitoring for any issues. If anything goes wrong, I would have a rollback plan ready to revert to the previous version quickly.

First, I would analyze the current system to pinpoint performance bottlenecks. Then, I would look into horizontal scaling by adding additional servers. Caching responses can help serve users faster and alleviate load on the database. Additionally, I'd ensure queries are optimized for efficiency and use load balancers to manage incoming traffic effectively.

First, I would verify the outage by checking the monitoring tools. Then, I would contact the on-call support to escalate and gather additional help. I would collect logs to analyze the issue and, if feasible, apply a temporary solution to get the service back online while documenting everything for a later review.

First, I would read the API documentation to understand the endpoints and data formats. Then, I would check the authentication requirements and implement any necessary security measures. After that, I would create a prototype to test data flow and handle any errors as they arise.

I would first explore the codebase to get a grasp of its architecture, focusing on main components. Then, I’d identify parts that need urgent updates and use Git to track any modifications. As I refactor, I’ll add comments for clarity and build documentation to reflect the current state of the system.

I would first acknowledge the client's issue and gather as much detail as possible about the problem. After that, I would try to replicate the issue in my development environment. Once I understand the root cause, I would document my findings and communicate them back to the client clearly, letting them know the next steps.

I would first collect detailed requirements from stakeholders to ensure I understand what they want. Then, I'd break the feature into smaller modules and select appropriate technologies. After that, I'd create a prototype to test the design. Finally, I would outline a strategy for testing and deployment based on user feedback.

To design an API, I would start by outlining the main resources, such as users and products. I'd define RESTful endpoints like /users and /products, applying CRUD operations. Authentication would use OAuth for security. I'd implement versioning in the URL, like /v1/users, to manage updates. Lastly, I'd ensure comprehensive documentation for easy developer onboarding.

To set up load balancing, I would choose a Layer 7 load balancer for HTTP traffic, implement health checks to ensure only healthy servers are used, and distribute server instances globally for redundancy. Additionally, I'd use a CDN to manage static resources efficiently.

First, I would set up a Git repository to manage version control. Then, I would choose a CI tool like Jenkins to automate the build and test process. I would create a pipeline that runs tests every time code is committed, ensuring that only passing code gets merged. Finally, I would configure automated deployment to a staging environment after successful tests, allowing for manual promotion to production.

I would start by mapping out all personal data flows in our systems to identify what we collect. Then, I would ensure any sensitive data is encrypted during transmission and storage. Access to this data would be restricted to only those who need it, following the principle of least privilege. Additionally, I would implement a clear data retention policy, deleting data that is no longer necessary.

In a recent project, I collaborated with front-end developers using Jira for task management. We held daily stand-ups to synchronize our work and address any blockers immediately. This ensured that our API was aligned with their expectations, leading to a smooth integration and timely delivery.

In a recent project, I encountered a bug where API requests were failing intermittently. I started by checking the server logs for error messages and used Postman to simulate requests. I found that the issue was caused by rate limiting on our API, triggered by too many concurrent requests. I implemented caching to reduce the number of requests and optimized our database queries. This resolved the issue and improved overall performance.

In a project, I disagreed with a teammate about using REST vs GraphQL for our API. I believed REST would be simpler for our backend. I initiated a discussion, laying out the pros and cons of each approach. We decided to implement a small prototype for both, which showed that REST was indeed more efficient for our needs. We learned to evaluate both methods together before making such decisions.

Yes, I led a team of 5 developers at my last job. I held daily stand-up meetings to maintain open communication and tracked our progress using Jira. When conflicts arose, I facilitated discussions to resolve them quickly. I set clear goals for each sprint, and made sure everyone was aligned with our deadlines.

In my last job, we decided to switch from MySQL to PostgreSQL to take advantage of its advanced features. I quickly familiarized myself with the new system by going through online tutorials and documentation. I built a small project to practice the differences in SQL syntax. The transition was seamless, and our application performance improved significantly as a result.