Amazon Java Developer Interview Questions & Answers

I first measured where the time was being spent instead of choosing a solution based on assumptions. I reviewed the Java code, database calls, and request flow and found that most of the delay came from an expensive database query that was executed repeatedly for data that changed infrequently. I then compared the three main options. Improving the query would reduce some cost and keep the design simple, but it would not remove repeated reads. Moving the work to an asynchronous flow could improve the user response, but it would add more components and make error handling more complicated. A cache could remove most repeated reads, but I needed to consider data freshness and cache invalidation. I discussed these tradeoffs with the team and proposed first improving the query and then adding a small application cache only for the data that was safe to reuse. I chose this approach because it addressed the root cause while keeping the design understandable. I added clear expiration rules so old data would not remain indefinitely, handled cache misses by reading from the database, and kept the database as the source of truth. I also added tests for both cached and uncached paths and reviewed the behavior with the team before releasing the change.

I first checked the evidence we did have, including application logs, request traces, thread activity, and recent deployment history. The strongest signal was that Java request threads were spending more time waiting for the downstream call. I confirmed that there had not been a recent code deployment that clearly explained the change. I then proposed temporarily bypassing a nonessential enrichment call and returning the core response without that extra data. This was a bold decision because we could not yet prove that the dependency was the root cause, and changing production behavior always carries risk. I chose this option because the enrichment was not required for the main transaction, the fallback behavior had already been supported by the service, and the change was easy to reverse. I explained the evidence, the uncertainty, the expected user impact, and the rollback plan to the team before making the change. After the change, I watched request behavior, logs, and downstream call patterns closely while another engineer continued investigating the dependency. The service became stable, which gave us stronger evidence that the slow dependency was a major contributor. Once the downstream service recovered, I restored the normal flow and verified that the service remained healthy.

I first checked the application logs, recent code changes, and monitoring data to understand what changed after the release. I noticed that the failures were connected to a new database access path. I compared the new behavior with the previous version and found that the new code was creating database queries much more often than expected. I shared this finding with the team so everyone was working from the same information. Instead of immediately changing several parts of the service, I suggested that we reduce risk by restoring the previous stable behavior first. We rolled back the affected change and verified that requests were processing normally again. After the service was stable, I reproduced the issue in a test environment and traced the Java code through the request flow. I found that data was being loaded repeatedly inside a loop instead of being fetched once and reused. I changed the logic so the required data was loaded efficiently, added tests that covered the problem case, and reviewed the fix with another developer before releasing it. I also documented what happened and discussed with the team how our review and testing process had missed the issue.

I first spoke with the developers and operations team members who normally handled releases. I asked them to walk me through the process and explain where problems usually happened. I found that the biggest issue was not one technical failure. It was that checks, commands, and recovery steps were stored in different places. I created one clear release checklist that covered application health checks, configuration verification, database change checks, and rollback steps. I also reviewed our existing deployment scripts and added simple validation where it was safe to do so, such as checking required configuration before a Java service was started. Before changing anything used by the wider team, I reviewed the changes with the operations team because they had deeper experience with the production environment. I then used the checklist during releases, collected feedback, and updated unclear steps. I also shared what I had learned with the rest of the development team so the process did not depend on me.