Interviews using ASP.NET Core frequently center on how apps function in actual production settings. Interviewers want to know how you manage slow APIs, big data sets, async problems, and external dependencies, not just syntax. In order to help you react confidently, this article explains frequently requested ASP.NET Core interview questions based on real-world performance settings with straightforward examples and excellent responses.
Let’s examine those below.
1. Your API is slow in production yet functions well locally. What do you look for first?
For instance
The API uses a tiny database to return data locally in 200 ms. Because the table has millions of records and operates via a network, the same API takes five seconds in production.
Explanation
I begin by looking at database queries and calculating the actual execution time. Before attempting a remedy, I enable logging to determine whether the bottleneck is a sluggish query, a configuration problem, or a resource limitation because production involves more data and network latency.
In addition to logging, I examine the query execution plan to determine whether the database is having trouble with ineffective joins or missing indexes. When a query is required to scan 10 million rows, it frequently turns into a catastrophe in production, even when it functions flawlessly on a local system with 10 rows. In order to prevent even basic queries from being slowed down, I also look for resource contention, such as excessive CPU consumption or memory strain on the database server.
2. You used async/await, but the API is still slow. Why?
Example
The API uses async methods, but inside the code it calls .Result on a database query.
Under load, requests start waiting for each other and the API becomes slow.
Explanation
While async and await are designed to improve scalability, they only help during I/O-bound tasks—like waiting for the database—by freeing up the server’s threads to handle other work. If you use blocking calls like .Result or .Wait() anywhere in code, you create a “Sync-over-Async” bottleneck. This forces a thread to sit idle and wait, which leads to thread-pool starvation where the API stops responding because no threads are left to handle new requests.
The fix is to ensure the entire call chain is asynchronous, meaning you never “block” the thread while waiting for data. Additionally, if the API is performing CPU-heavy work—like processing large amounts of data in memory—async will not make that part faster because the CPU is actually busy, not just waiting. I ensure that all database calls use await and avoid any code that stops a thread from moving on to the next task.
3. Your API returns thousands of records. How do you improve performance?
Example
A GET /users API returns 50,000 user records in one response, making the API slow and the UI unresponsive.
Explanation
Returning thousands of records in a single response is a major performance issue because it consumes significant amounts of memory and bandwidth on both the server and the client. To improve this, I implement server-side pagination and filtering so the API only delivers a small, manageable chunk of data at a time. I also use projections to return only the specific fields required by the UI, rather than the entire database object, which significantly reduces the payload size.
4. Your API calls another API. What if that API fails?
Example
An order API depends on a third-party payment service.
If the payment service is slow or down, the order API keeps waiting and eventually fails.
Explanation
When an API depends on a third-party service, you are dependent on their uptime and speed. If that external service becomes slow or goes down, your own API will “hang” while waiting for a response, eventually exhausting its resources and failing. To prevent this, I never allow an external call to run indefinitely; instead, I implement timeouts and retry policies to handle failures gracefully without crashing the application.
To build a resilient system, external failures should be treated as an expected event rather than an exception. By using a library like Polly, you can define a “Circuit Breaker” that stops calling the failing service for a short period, allowing it to recover. These calls should also be wrapped in a try-catch block to return a meaningful, user-friendly error message or a fallback result rather than letting the entire request time out or crash.
By adding these safeguards, you ensure that one slow dependency does not cause a cascading failure across the entire platform. The API remains responsive and provides clear feedback to the user, even when external partners are struggling.
5. How do you prevent sensitive data from being returned in API responses?
Example
A user table contains fields like password hashes, internal flags, and audit data.
Returning the full entity exposes sensitive information.
Explanation
Returning database entities directly is a major security risk because it often exposes sensitive fields such as password hashes, internal administrative flags, or private audit data that the end user should never see. Even if the UI does not display these fields, they are still sent over the network in the JSON response, where they can be intercepted or viewed through browser developer tools.
To prevent these data leaks, I always use Data Transfer Objects (DTOs) or ViewModels to strictly control exactly which pieces of information leave the server.
The fundamental rule of secure API design is: the database schema is for the server, and the DTO is for the client. By keeping them separate, you ensure that adding a sensitive internal column to the database in the future will not accidentally expose it through the API.
Conclusion
In this article, we explored how small patterns in EF Core and async code can lead to major bottlenecks in production. Performance-related questions are common in interviews because they test your ability to handle real-world scale, data security, and external dependencies.
By mastering these simple fixes, you demonstrate that you do not just write code that works locally, but code that is resilient and efficient in real-world production environments.
I hope this helps.
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