Apple .NET Developer Interview Questions & Answers

I first checked our application logs, health checks, and recent deployment history to understand when the failures started and which requests were affected. I compared successful requests with failed ones and saw that the errors were concentrated around a database call made by one API path. I then reviewed the latest production change and found that a new query pattern was putting much more pressure on the database than expected. I shared this finding with the team so everyone had the same picture of the incident. For mitigation, I chose the lowest risk option. I rolled back the application change that introduced the query instead of trying to create a complex fix while users were still affected. I watched the service logs and database behavior after the rollback and confirmed that requests were returning to normal. Once the service was stable, I reproduced the problem outside production and examined the generated SQL and data access code. I found that the new implementation was loading more data than the request needed and then filtering it in application code. I changed the query so filtering happened in the database and only the required data was returned. I added tests for the affected path and reviewed the fix with another developer before deploying it. After deployment, I monitored the same logs and health signals to confirm that the original failure pattern did not return. I also documented the incident, including the timeline, root cause, mitigation, and permanent fix. Finally, I updated our review process so changes to important database queries received extra attention before production, and I added monitoring around this API path so similar behavior would be detected earlier.

I first separated the work into what was required for a safe release and what was useful but optional. I kept request validation, error handling, authorization checks, logging, and automated tests for the main success and failure paths. I did not remove those items because defects there could affect production behavior or make problems difficult to diagnose. I proposed postponing a broader refactoring of several service classes and some cleanup that was not needed for the new behavior. I explained the tradeoff to the team and made the deferred work visible so it would not be forgotten. I then kept the code change small, reused the existing service patterns, and avoided introducing a new abstraction just to make the design look cleaner. Before release, I reviewed the important paths with another developer, ran the existing test suite, added focused tests for the new behavior, and checked the application logs in our test environment. This gave us enough confidence to ship quickly while still protecting the parts of code quality that directly affected correctness and supportability.

I proposed moving the long running work out of the request path and placing it on a message queue for a background .NET worker to process. The risk was that queue delays, duplicate messages, or worker failures could create new problems that our test environment might not reveal. Before making the change, I explained those risks to the team and described how I planned to control them. I made the consumer idempotent, which meant processing the same message more than once would not create duplicate results. I added retry handling for temporary failures and made failed messages visible for investigation instead of silently losing them. I also added logging and monitoring around queue depth, processing failures, and completion so we could see how the design behaved under real traffic. I kept the existing synchronous path available behind a feature flag so we could return to it quickly if the new approach caused problems. We tested failure cases together, reviewed the rollout plan, and enabled the new path gradually while I watched the application and queue behavior closely.