Question 1
Difficulty: easy
How do you design a REST API that is easy for other developers to understand and use?
Sample answer
I start by thinking about the developer experience, not just the data model. I use clear, resource-based URLs, consistent naming, and standard HTTP methods so the API feels predictable. For example, I would use nouns for endpoints, keep collections and individual resources separate, and make sure response structures are stable across the API. I also define request and response schemas early, document required fields clearly, and include examples for common use cases. In my experience, good error messages are just as important as good success responses, because they reduce support requests and speed up integration. I also pay attention to pagination, filtering, and sorting so the API stays usable as data grows. Before release, I like to review the API with a developer mindset: if someone saw it for the first time, could they guess how to use it without asking for help? That is usually the best sign of a well-designed REST API.
Question 2
Difficulty: medium
What is your approach to versioning a REST API without breaking existing clients?
Sample answer
My first goal is always to avoid breaking changes whenever possible. If a change can be introduced in a backward-compatible way, I prefer that over creating a new version immediately. For example, I may add optional fields, new endpoints, or new response values while keeping existing behavior intact. When a breaking change is unavoidable, I use a clear versioning strategy and communicate the timeline early so client teams can plan. I also keep the old version supported for a reasonable period and track usage so I know when it is safe to retire it. In practice, versioning is not just a technical decision, it is also about change management. I try to pair version changes with good documentation, migration examples, and deprecation notices. I have found that clients are much more comfortable with API evolution when they know what changed, why it changed, and how long they have to adjust.
Question 3
Difficulty: easy
How do you decide when to use GET, POST, PUT, PATCH, and DELETE in a REST API?
Sample answer
I use the HTTP method that best matches the intent of the operation and keep that behavior consistent. GET is for retrieving data and should not change state. POST is useful for creating a new resource or for actions that do not fit clean CRUD patterns. PUT is my choice when replacing a resource or when the client should send the full object representation. PATCH is better when only part of the resource needs to change, because it avoids sending unnecessary data. DELETE is straightforward for removing a resource, but I still think about how the system should respond if the resource is already gone or if deletion is soft instead of permanent. I also make sure the method behavior is intuitive for consumers, because unclear semantics create bugs in client integrations. In addition, I pay attention to idempotency. If a request might be retried by a client or gateway, the API should behave safely and predictably.
Question 4
Difficulty: medium
Tell me about a time you had to troubleshoot a difficult API bug in production. How did you approach it?
Sample answer
In production, I treat API bugs as both a technical issue and a communication issue. My first step is usually to narrow the problem using logs, request IDs, and any monitoring we have in place. I want to understand whether the failure is isolated to one endpoint, one client, one payload pattern, or one backend dependency. Once I have a pattern, I reproduce the issue in a lower environment if possible, because that helps confirm the root cause instead of guessing. I also check recent deployments and schema changes, since many API issues come from a small change that had a wider impact than expected. One issue I worked on involved a serialization mismatch between two services that only appeared when a nullable field was missing. We fixed it by tightening validation, improving tests, and adding better contract checks. I always try to leave the system more observable than before so the same type of issue is easier to detect next time.
Question 5
Difficulty: hard
How do you handle authentication and authorization in REST APIs?
Sample answer
I separate authentication from authorization very clearly. Authentication confirms who the caller is, while authorization determines what that caller can do. For most modern APIs, I prefer token-based approaches such as OAuth2 or JWT depending on the use case and security requirements. I always think about token lifetime, revocation, scopes, and refresh behavior, because a secure API is not only about logging in, it is about managing access over time. On the authorization side, I use role-based or permission-based checks depending on the complexity of the business rules. I also make sure sensitive endpoints return safe error responses so they do not reveal too much information. Security testing matters too, so I verify things like expired tokens, invalid signatures, privilege escalation attempts, and missing claims. I try to design the system so that security controls are enforced consistently at the API layer, not scattered in a way that is easy to miss.
Question 6
Difficulty: medium
What would you do if a frontend team says your API is too slow or difficult to work with?
Sample answer
I would treat that feedback as valuable, not defensive. First, I would ask for specific examples so I can understand whether the problem is latency, payload size, endpoint design, or inconsistent behavior. Then I would look at the API metrics and trace data to confirm where the bottleneck is. Sometimes the issue is in the backend query, but sometimes the real problem is that the API is forcing the frontend to make too many requests or to process more data than necessary. In that case, I might introduce filtering, pagination, field selection, or a more focused endpoint. I also like to sit with the consuming team and watch how they use the API in practice, because that often reveals usability issues that are hard to see from the server side. My goal is not just to defend the current implementation, but to improve the integration experience. A good API should help the client move faster, not slow them down.
Question 7
Difficulty: medium
How do you ensure your REST API is reliable and handles errors consistently?
Sample answer
I focus on predictable behavior. That means using a consistent error format, meaningful status codes, and messages that help the client understand what went wrong without exposing internal details. I make a point of validating input early so bad requests fail fast and clearly. I also think about edge cases such as timeouts, dependency failures, duplicate requests, and partial updates. For reliability, I try to design idempotent operations where appropriate and ensure retries do not create duplicate records or inconsistent state. From a delivery perspective, I rely on automated tests at multiple levels: unit tests for business rules, integration tests for service interactions, and contract tests for API expectations. Observability is part of reliability too, so I want good logs, metrics, and alerting around error rates and latency spikes. In my experience, a reliable API is one where both the server and the consumer can recover gracefully when something goes wrong.
Question 8
Difficulty: hard
How do you balance REST best practices with real business requirements that do not fit neatly into CRUD operations?
Sample answer
I think REST should be a guide, not a rigid constraint that blocks the business. When a requirement does not fit clean CRUD operations, I look for the cleanest resource model that still reflects the domain. Sometimes that means introducing a subresource, an action endpoint, or a state transition that maps better to the business process. I try to avoid overly generic endpoints that become hard to understand later. At the same time, I do not force a pure REST shape if it makes the API awkward for consumers. For example, if a workflow represents approval, submission, or cancellation, I may model it as a state change with clear rules rather than pretending it is just a simple update. The key is consistency and clarity. I want the API to match the business language as closely as possible so teams can reason about it easily. If I can explain the endpoint in one sentence, that is usually a good sign.
Question 9
Difficulty: medium
Describe how you would test a REST API before releasing it.
Sample answer
I like to test REST APIs at several levels because no single test type catches everything. At the unit level, I verify business logic, validation, and transformation rules. At the integration level, I test the API with real or realistic dependencies so I can catch serialization issues, database behavior, and configuration problems. I also use contract testing when multiple teams depend on the API, because that helps prevent accidental breaking changes. For critical endpoints, I include performance checks and negative tests, such as invalid input, missing authentication, expired tokens, and duplicate submissions. I also review the API from the consumer’s point of view by looking at request examples and response clarity. Automated tests are essential, but I do not rely on them alone. I want to manually validate the most important paths before release, especially if the endpoint affects payments, user access, or other sensitive workflows. That combination gives me confidence that the API is functional and practical.
Question 10
Difficulty: easy
If you joined our team, how would you quickly get up to speed on an existing REST API codebase?
Sample answer
I would start by understanding the domain and the current API usage before changing anything. First, I would read the documentation, inspect the endpoints, and identify the most important flows used by real clients. Then I would trace one or two complete request paths through the code so I can see how validation, business logic, persistence, and error handling are connected. I also like to review logs, metrics, and recent tickets because they reveal where the pain points are. If the codebase is large, I focus on the highest-impact areas first instead of trying to understand every file at once. I would also ask the team about known design decisions, technical debt, and areas that are intentionally temporary. That context saves a lot of time. My goal in the first few days is not to rewrite everything, but to become useful quickly while avoiding accidental changes that could break existing consumers. Good onboarding is about learning the system as it actually works, not as the architecture diagram says it works.
