This article covers real-world scenario-based interview questions focused on production debugging, Splunk log analysis, and troubleshooting in Java/Spring Boot microservices. These scenarios are commonly asked in backend interviews for 3–5 years experienced developers.
1. Splunk shows ValidationException: password length < 8. Users cannot register. What will you do?
Answer: First, verify if the validation rule changed in the latest deployment. Check recent commits and configuration changes. Reproduce the issue in staging. Validate request payloads from frontend. If the rule is incorrect, roll back or hotfix. Add proper error messages and monitoring alerts.
2. NullPointerException appears in production but not locally. How do you debug?
Answer: Check production input data from logs. Compare environment configurations. Add defensive null checks and enhanced logging. Use correlation IDs to trace requests and reproduce using production-like data.
3. Sudden spike in 500 errors after deployment.
Answer: Check deployment logs, recent code changes, and health metrics. Roll back if impact is high. Analyze stack traces in Splunk and identify the failing component.
4. OutOfMemoryError is occurring repeatedly.
Answer: Capture heap dumps, analyze memory leaks, review GC logs, and check object retention. Increase heap size temporarily and optimize memory usage.
5. Frequent database connection timeouts.
Answer: Check connection pool configuration, DB load, slow queries, and network latency. Tune pool size and optimize queries.
6. API responses are slow (>10 seconds).
Answer: Analyze slow endpoints, DB queries, external API calls, and thread usage. Add caching or optimize bottlenecks.
7. Deadlock errors in database logs.
Answer: Identify conflicting transactions, reduce lock duration, reorder queries, and implement retry mechanisms.
8. Authentication failures for valid users.
Answer: Verify token validation, clock synchronization, cache issues, and credential stores.
9. Duplicate records inserted in production.
Answer: Check concurrency handling, idempotency logic, and DB constraints. Add unique indexes and request deduplication.
10. API works in staging but returns 403 in production.
Answer: Review security configurations, firewall rules, API gateway policies, and environment variables.
11. Feign client timeout between microservices.
Answer: Check service health, network latency, timeout settings, and circuit breaker status.
12. Circuit breaker opens frequently.
Answer: Indicates downstream service instability. Investigate root cause and adjust thresholds.
13. Kafka consumer lag increasing.
Answer: Scale consumers, optimize message processing, and monitor broker performance.
14. High CPU usage causing crashes.
Answer: Capture thread dumps, identify hot threads, and optimize CPU-intensive logic.
15. Inconsistent data across services.
Answer: Use distributed tracing and correlation IDs to track request flow.
16. Transaction partially committed.
Answer: Ensure proper transactional boundaries and rollback configuration.
17. Lock wait timeout exceeded.
Answer: Optimize queries and reduce transaction duration.
18. Sudden DB query slowdown.
Answer: Analyze execution plans and add indexing.
19. Data corruption after deployment.
Answer: Roll back changes, restore backups, and run integrity checks.
20. Batch job fails intermittently.
Answer: Add retries, logging, and validate input data.
21. Missing features after deployment.
Answer: Verify deployment version and cache invalidation.
22. App crashes in Docker but not locally.
Answer: Check container resource limits and environment configs.
23. Memory leak patterns detected.
Answer: Use heap analysis tools and fix object retention.
24. Load balancer uneven traffic.
Answer: Review balancing algorithm and health checks.
25. Errors increase after scaling.
Answer: Check shared resources and session handling.
26. Repeated invalid input attacks.
Answer: Implement rate limiting and input validation.
27. JWT validation failures.
Answer: Check secret keys, expiration, and clock sync.
28. Sensitive data appearing in logs.
Answer: Mask sensitive fields and update logging policies.
29. Thread pool exhaustion.
Answer: Increase pool size and optimize blocking calls.
30. Increasing GC pauses.
Answer: Tune JVM GC settings and reduce object creation.
31. Latency spikes during peak hours.
Answer: Implement autoscaling and caching.
32. Frequent client retries detected.
Answer: Investigate server errors and network stability.
Conclusion: Scenario-based questions test real production troubleshooting skills. Strong answers should show a structured debugging approach: analyze logs, isolate the root cause, reproduce the issue, fix safely, and add preventive monitoring.
