Question 1
Difficulty: medium
How do you design an API so it is easy for developers to use and maintain over time?
Sample answer
I start by treating the API as a product, not just a technical layer. That means I focus on clear resource naming, predictable request and response structures, and consistent error handling from the beginning. I usually align the design with business use cases first, then map those to clean endpoints rather than exposing internal database structures. I also pay close attention to versioning, pagination, filtering, and authentication because those choices affect developer experience long after launch. Before finalizing anything, I like to validate the design with a small group of consumers or by writing example requests and responses myself to see if the API feels intuitive. I also document edge cases clearly, especially around validation and rate limits. For maintainability, I keep the implementation modular, separate service logic from transport concerns, and make sure changes are backward compatible whenever possible. In practice, that approach reduces support questions and makes the API easier to evolve safely.
Question 2
Difficulty: medium
Tell me about a time you had to debug a difficult API issue in production. How did you approach it?
Sample answer
When I run into a production API issue, I try to be calm and systematic instead of jumping to conclusions. In one case, users were seeing intermittent timeouts on a high-traffic endpoint, but only during certain peak periods. I first checked whether the issue was isolated to one service or one region, then reviewed logs, latency metrics, and recent deployments. That helped me narrow it down to a downstream dependency that was occasionally slowing responses beyond our timeout threshold. Instead of just increasing the timeout, I worked with the team to add circuit breaking, improve retry behavior, and cache a few expensive lookups. I also added better tracing so we could see where requests were spending time. What I learned was that production issues usually have multiple contributing factors, so I like to fix the immediate symptom while also addressing the underlying design weakness. That approach has helped me prevent repeat incidents.
Question 3
Difficulty: medium
How do you handle API versioning when you need to make a breaking change?
Sample answer
I try to avoid breaking changes whenever possible, but when they are necessary, I plan the transition carefully. First, I identify exactly what is breaking and whether there is a way to support both the old and new behavior for a period of time. If a new version is required, I prefer a clear versioning strategy, such as a versioned path or header, depending on the product’s conventions. I then publish migration guidance early, including concrete examples of what changes for consumers and how to adapt. I also track which clients are still using the old version so I can coordinate deprecation timelines realistically. In one project, we introduced a new response structure while keeping the old one available for several months, which gave partner teams enough time to update without disruption. My goal is always to balance technical progress with consumer stability, because an API is only successful if people trust that it will not surprise them.
Question 4
Difficulty: hard
What steps do you take to secure an API?
Sample answer
API security starts with assuming every endpoint will be probed or misused at some point. My first priorities are authentication, authorization, and transport security. I make sure the right identity mechanism is in place, and then I enforce access control at the resource and action level rather than relying on the client to behave properly. After that, I look at input validation, rate limiting, and protection against common abuse patterns like excessive requests, injection attempts, or broken object-level authorization. I also like to keep secrets out of code, use short-lived tokens where possible, and log security-relevant events without exposing sensitive data. For public or partner APIs, I pay extra attention to scope design and least privilege. On the operational side, I want monitoring in place so unusual traffic patterns are visible quickly. Security is not something I add at the end; I build it into the API design, testing, and deployment process from the start.
Question 5
Difficulty: hard
How do you decide between REST, GraphQL, and other API styles for a project?
Sample answer
I choose the API style based on the problem, the consumers, and the operational needs rather than personal preference. REST works well when the domain is resource-oriented, the access patterns are predictable, and I want simplicity and broad compatibility. GraphQL can be a good fit when clients need flexible data fetching and the problem is mostly about reducing over-fetching or under-fetching across many screens. That said, GraphQL adds complexity in areas like authorization, caching, and query cost control, so I only recommend it when the benefits are clear. For internal service-to-service communication, I also consider RPC-style approaches if the commands are more action-oriented. In practice, I ask questions like: who are the clients, how often will the contract change, what are the performance constraints, and how much tooling does the team support? I’d rather choose a slightly less trendy option that solves the real business problem cleanly than adopt a style that creates ongoing operational friction.
Question 6
Difficulty: medium
Describe how you would design pagination, filtering, and sorting for a large API collection endpoint.
Sample answer
For large collection endpoints, I prefer to design pagination, filtering, and sorting in a way that is consistent and predictable for developers. If the dataset can change frequently or the collection is large, cursor-based pagination is often safer than offset-based pagination because it handles inserts and deletes more reliably. I usually include a stable sort key so clients get consistent results between requests. For filtering, I keep the supported fields explicit and documented, and I avoid allowing arbitrary query complexity unless there is a strong need and strong validation in place. Sorting should also be limited to fields that are indexed or otherwise performant, because a flexible API is not helpful if it becomes slow under load. I also make sure the response includes metadata that helps clients understand whether more data is available and how to request the next page. The key is to keep the interface intuitive without sacrificing performance or correctness.
Question 7
Difficulty: easy
How do you ensure your APIs are well documented and easy for other teams to consume?
Sample answer
I treat documentation as part of the deliverable, not something to clean up later. The most useful API docs are practical: clear endpoint descriptions, example requests and responses, authentication requirements, error codes, and realistic usage notes. I like to write documentation alongside the code or design, because if I cannot explain an endpoint clearly, that usually means the design needs work. I also think examples matter a lot more than many teams expect, especially for first-time consumers. Where possible, I generate docs from source definitions or keep them close to the implementation so they stay current. I’ve also found it helpful to include common workflows rather than only listing endpoints, because developers usually want to accomplish a task, not memorize a spec. Before release, I often ask someone unfamiliar with the project to read the docs and try a basic integration. If they get stuck, that tells me where the documentation needs improvement.
Question 8
Difficulty: easy
Tell me about a time you had to work with backend, frontend, or QA teams to deliver an API successfully.
Sample answer
In one project, the API itself was straightforward, but success depended on close coordination across teams. We were building a new order workflow, and the frontend team needed stable response shapes early to build the UI, while QA needed testable scenarios and the backend team was still refining the business rules. I organized short working sessions instead of relying only on ticket comments, which helped us catch mismatched assumptions quickly. For example, we discovered that the frontend expected a single error message, while the backend was returning field-level validation errors. We agreed on a format that supported both. I also shared sample payloads and edge-case examples so QA could create better test coverage before final implementation. The main lesson for me was that API development is collaborative by nature. When teams communicate early and concretely, you avoid late surprises and reduce rework. I try to keep that coordination lightweight but frequent, especially when multiple consumers depend on the same contract.
Question 9
Difficulty: medium
How do you approach API testing to make sure an endpoint is reliable before release?
Sample answer
I like to think about API testing at several levels. First, I write unit tests around core business logic so I know the service behaves correctly under normal and edge conditions. Then I add integration tests to validate the API layer, persistence, authentication, and any downstream dependencies. For critical endpoints, I also create contract-focused tests that verify request and response shapes so consumers are protected from accidental breaking changes. Beyond that, I test negative scenarios deliberately: invalid inputs, unauthorized access, missing fields, rate limits, and timeout behavior. If the API touches other services, I like to use test doubles or controlled environments so I can isolate failures and understand what the endpoint actually depends on. I also pay attention to performance testing for high-traffic routes, because a functionally correct endpoint can still fail if it does not scale. My goal is to have enough automated coverage that releases feel routine rather than risky.
Question 10
Difficulty: hard
If an API consumer says your endpoint is too slow, how would you investigate and improve it?
Sample answer
I would start by clarifying what “slow” means in context: average latency, p95 latency, time to first byte, or occasional spikes. Once I know the pattern, I would compare request traces, logs, and metrics to see whether the delay is inside the API layer, in a database query, in downstream calls, or in serialization and transformation logic. I also like to reproduce the issue with representative data because performance problems often appear only at scale or with certain inputs. If the bottleneck is a query, I examine indexing and query shape first. If it is a downstream dependency, I look at caching, batching, parallelism, or fallbacks. Sometimes the fix is architectural, and sometimes it is just removing unnecessary work from the response path. I also want to validate the improvement with numbers, not assumptions, so I measure before and after changes. In my experience, the best performance work combines profiling, practical tradeoffs, and close attention to real usage patterns.
