Question 1
Difficulty: medium
How do you decide when GraphQL is a better choice than REST for a project?
Sample answer
I usually choose GraphQL when the frontend needs flexibility and the data model is complex enough that multiple REST endpoints would become hard to maintain. For example, if a product page needs data from catalog, pricing, inventory, reviews, and user-specific personalization, GraphQL can reduce over-fetching and make the client much easier to evolve. I also look at team structure and caching needs. If the API is mostly simple CRUD with stable resources, REST may be perfectly fine and cheaper to operate. I don’t treat GraphQL as a default replacement. In a strong GraphQL setup, the value comes from letting clients ask for exactly what they need, while keeping the backend organized around domain logic. I also think about versioning, authorization complexity, and observability. If those concerns are manageable and the client benefits are real, GraphQL is usually the better fit.
Question 2
Difficulty: medium
Describe how you would design a GraphQL schema for a product catalog and shopping cart.
Sample answer
I would start by modeling the domain around the user’s needs rather than mirroring the database tables. For a catalog and cart, I’d define types like Product, Variant, Price, InventoryStatus, Cart, CartItem, and maybe Promotion if discounts matter. The key is to make the schema intuitive for the client and consistent for the business. I’d expose queries such as product(id), products(filter, sort, pagination), and cart(id), then mutations like addItemToCart, updateCartItemQuantity, and removeCartItem. I prefer input types for mutations so validation stays clear and the API stays predictable. I’d also pay attention to pagination strategy, probably cursor-based for large product lists. On the resolver side, I’d keep business rules in services, not inside the resolvers. That keeps the GraphQL layer thin, testable, and easier to maintain as the catalog grows or pricing logic changes.
Question 3
Difficulty: hard
How do you prevent the N+1 problem in GraphQL resolvers?
Sample answer
The N+1 problem is one of the first performance issues I watch for in GraphQL. It usually shows up when a query for a list of items triggers separate calls for each nested field, which can become expensive very quickly. My first step is to inspect resolver patterns and identify repeated lookups, especially in nested fields like user, author, inventory, or permissions. I typically use batching and caching tools such as DataLoader, or implement a custom batching layer if the architecture requires it. The goal is to collect all requested IDs, fetch them in one call, and then map the results back to the original records. I also try to design the schema so resolvers can pull data efficiently from the service layer instead of making many small database calls. Beyond that, I use query complexity checks and profiling to catch performance regressions early. Preventing N+1 is really a combination of good schema design, resolver discipline, and measuring real query behavior.
Question 4
Difficulty: medium
Tell me about a time you optimized a slow GraphQL API.
Sample answer
In a previous project, a client dashboard started timing out because a single GraphQL query was pulling account details, recent activity, notifications, and permissions in one request. At first the schema looked fine, but profiling showed the response was triggering too many downstream calls and one expensive join in the database. I worked with the team to trace the query path and found that the nested resolvers were fetching the same user context repeatedly. I introduced batching with DataLoader, moved a few expensive lookups into a service that could fetch data in bulk, and added caching for short-lived reference data. I also simplified part of the schema so the client requested heavy fields only when needed. After the changes, average response time dropped significantly and the timeouts disappeared. What I learned was that GraphQL performance problems are often architectural, not just code-level. You have to look at the query shape, resolver behavior, and underlying data access together.
Question 5
Difficulty: hard
How do you handle authentication and authorization in GraphQL?
Sample answer
I handle authentication at the request layer and authorization at the field or resolver level, depending on the sensitivity of the data. Authentication usually means verifying the token or session once, attaching the user context to the request, and making that context available to resolvers. Authorization is more nuanced because GraphQL allows clients to request many fields in one query. I don’t rely only on route-level checks since there is usually just one endpoint. Instead, I enforce permissions in shared guard logic, schema directives, or resolver middleware so access rules are consistent. For example, a user might be allowed to read their own orders but not someone else’s, or an admin might be able to access additional fields on a customer record. I also make sure errors are handled carefully so we don’t leak sensitive details. Good GraphQL security is about least privilege, clear policy definitions, and testing permissions as thoroughly as functional behavior.
Question 6
Difficulty: medium
What would you do if a frontend team asked for a field that you think should not be exposed in the schema?
Sample answer
I’d first try to understand why they need the field and what problem they are solving. In GraphQL, it’s easy to expose too much because the schema feels convenient, but every field becomes part of the contract and increases security and maintenance concerns. If the field contains sensitive data, internal identifiers, or implementation details, I’d explain the risk clearly and suggest an alternative that gives them the business outcome without exposing the raw value. Sometimes the right answer is a derived field, a more specific type, or a separate mutation that handles the operation safely. I’ve found that these conversations go best when I’m not just saying no. I try to offer a better API shape and show how it keeps the schema cleaner long term. If the field still needs to exist, I’d involve product and security stakeholders, document the decision, and make sure access controls and audit logging are in place.
Question 7
Difficulty: medium
How do you version or evolve a GraphQL schema without breaking clients?
Sample answer
One of the strengths of GraphQL is that you can evolve the schema without forcing a full version bump every time, but that only works if you’re disciplined. My approach is to prefer additive changes: add new fields, new input values, or new types while keeping existing ones stable. If a field needs to change shape, I usually introduce the new field first, mark the old one as deprecated, and communicate the migration path clearly to consumers. I also try to design types with future growth in mind so we don’t paint ourselves into a corner. For example, if a field is likely to need more detail later, I might model it as an object instead of a scalar from the beginning. I keep schema change logs, monitor field usage, and avoid removing deprecated fields until I’m confident the clients have moved. Evolution works best when you combine technical deprecation support with active coordination across frontend and backend teams.
Question 8
Difficulty: medium
How do you test GraphQL APIs effectively?
Sample answer
I like to test GraphQL at multiple layers because a single test type never gives enough confidence. At the unit level, I test resolver logic, validation rules, and authorization checks in isolation so business behavior is clear. At the integration level, I run actual GraphQL queries against a test schema to verify that nested fields, error handling, and data fetching work together correctly. I also use schema validation and contract-style tests to make sure required fields and types don’t change unexpectedly. For more complex APIs, I add tests for performance-sensitive queries and edge cases like pagination, null values, and partial failures. Another area I pay attention to is the shape of error responses, because GraphQL can return partial data with errors, and clients need predictable behavior. I prefer tests that read like real client queries, since that catches schema or resolver issues earlier. Overall, I want a test suite that protects both functionality and developer experience.
Question 9
Difficulty: hard
Explain how you would troubleshoot a GraphQL query that returns partial data and errors.
Sample answer
I’d start by checking whether the partial data is expected behavior or a symptom of a deeper issue. GraphQL can return some fields successfully while others fail, so the first thing I inspect is the error array and the path values to see exactly which resolver broke. Then I’d look at whether the failure is due to missing data, a permissions check, a downstream service outage, or an exception in a nested resolver. If the failure is in one field, I’d trace that resolver and compare the behavior with the surrounding fields to see whether the issue is isolated or systemic. I also check whether the schema defines the field as nullable, because that affects how errors propagate. In production, logs and tracing are essential because the client only sees part of the picture. My goal is to identify whether the API is behaving correctly with a graceful fallback or whether we have a real reliability bug that needs fixing in the resolver or upstream service.
Question 10
Difficulty: hard
What do you consider best practices for building GraphQL APIs that will scale with more teams and more traffic?
Sample answer
For scale, I think about both technical and organizational design. On the technical side, I keep the schema domain-driven, use clear naming, and avoid putting too much logic into resolvers. Shared services, batching, caching, query depth limits, and complexity analysis all matter once traffic grows. I also make observability a first-class concern, so we can see slow fields, expensive queries, and failure patterns before they become incidents. On the team side, I like strong schema ownership and documentation. If multiple teams contribute to the API, there needs to be a process for reviewing changes, communicating deprecations, and preventing schema sprawl. I also prefer having standards for pagination, error handling, and authorization so every part of the API feels consistent. In my experience, scalable GraphQL isn’t just about performance tuning. It’s about building an API platform that other teams can trust, extend, and operate safely as the product grows.
