This is where I spent most of my time and honestly probably over-indexed on the API gateway and load balancer pieces before getting to the more interesting stuff.
Start by clarifying requirements: expected request volume, latency SLAs, cost constraints, and whether streaming is needed. Then present a layered architecture: a stateless gateway service that handles auth, rate limiting, request routing, and provider abstraction, with a primary provider and a fallback provider configured via a circuit breaker. Walk through the request lifecycle, failure handling, and observability, and discuss trade-offs like synchronous vs asynchronous fallback and caching.
Pro tip: Emphasize idempotency and graceful degradation: use idempotency keys to safely retry on fallback, and consider returning a cached or canned response if both providers fail, rather than a hard error. This shows you think about user experience and system resilience beyond just failover.
Ask about expected QPS, latency SLAs, cost sensitivity, streaming support, and whether responses need to be cached. This ensures your design addresses the right priorities.
Outline the gateway's role: authentication/authorization, rate limiting, request validation, provider abstraction, routing, and observability. Keep it stateless for horizontal scaling.
Describe how requests go to the primary provider, with a circuit breaker that trips on errors or latency thresholds and routes to the fallback. Discuss retry policies, timeouts, and idempotency.
Walk through a request: from client to gateway, auth, rate limit, primary call, fallback on failure, and response. Cover edge cases like partial failures, streaming fallback, and caching.
Explain monitoring (metrics, logs, traces), alerting, and how to scale the gateway. Discuss trade-offs: synchronous vs asynchronous fallback, cost vs reliability, and complexity of multi-provider support.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the purpose of the request log (observability, debugging, auditing, rate limiting) and the gateway's context. Then propose a minimal but extensible set of fields grouped by category (request metadata, response metadata, timing, error, and context), explaining the rationale for each. Emphasize trade-offs like storage cost, privacy, and query performance.
Pro tip: Mention that you'd avoid logging sensitive data like auth tokens or PII, and suggest using a correlation ID to tie logs across services. Also note that fields should be chosen based on what you need to answer common operational questions (e.g., 'which endpoint is slow?').
Ask or state the primary use cases: debugging, monitoring, auditing, or analytics. This determines which fields are essential versus nice-to-have.
Include fields like timestamp, request ID, method, path, status code, and client IP. These are fundamental for tracing and basic analysis.
Capture latency (total, upstream, gateway processing) to enable performance monitoring and bottleneck identification.
Log error codes/messages, user ID (if available), and correlation IDs to support debugging and cross-service tracing.
Address storage costs, privacy (avoid PII), and how to extend the schema (e.g., via JSON metadata) without breaking existing queries.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with append-only log storage, something like S3 with object lock or a write-once audit table.
Start by clarifying the audit requirements (what to log, who needs access, compliance needs) and then propose a layered architecture: capture immutable logs at the gateway, store them in a write-once-read-many (WORM) system, enforce retention policies, and implement fine-grained access control. Emphasize trade-offs between performance, cost, and security, and mention how you'd handle sensitive data and scale.
Pro tip: Mention that audit logs should be stored separately from application data with strict access controls, and that you'd use cryptographic hashing or digital signatures to ensure tamper-evidence. Also, consider using a dedicated audit service or sidecar to avoid impacting gateway performance.
Ask questions to understand what needs to be audited (e.g., all requests, only sensitive operations), who needs access (compliance, security, developers), and any regulatory requirements (GDPR, HIPAA, SOC2).
Ensure every request/response is logged with sufficient detail (timestamp, user, action, resource, status) and that logs are written to an append-only, tamper-evident store (e.g., WORM storage, blockchain-like hash chain).
Specify how long logs are kept based on compliance and business needs, and automate archival and deletion. Consider tiered storage (hot/warm/cold) to balance cost and accessibility.
Enforce least-privilege access to audit logs using RBAC/ABAC, and log all access to the audit logs themselves. Set up alerts for suspicious access patterns.
Discuss how to handle high throughput without impacting gateway latency, e.g., asynchronous logging, buffering, and partitioning. Mention trade-offs between synchronous vs asynchronous logging.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This came at the end and felt like a stress test.
Start by acknowledging that the new requirements are cross-cutting concerns and should be layered as middleware or decorators around the existing LLM pipeline, not baked into core logic. Then walk through each concern—caching, PII redaction, and evaluation—explaining where it fits in the request/response flow and how to introduce it with minimal disruption. Emphasize incremental rollout, feature flags, and observability to validate each layer independently.
Pro tip: Frame the solution as a pipeline of composable interceptors, and mention that you'd start with PII redaction because it's a compliance risk, then caching for cost/latency, and evaluation last since it's non-blocking. This shows you prioritize by business impact, not just technical elegance.
Ask about latency budgets, data sensitivity, evaluation frequency, and whether caching should be exact-match or semantic. Confirm that the existing system can be extended without breaking current contracts.
Propose a pipeline where each concern is an independent interceptor: PII redaction on input, caching around the LLM call, and evaluation asynchronously after the response. This keeps core logic untouched and allows enabling/disabling layers via config.
Explain the correct order: redact PII before caching to avoid storing sensitive data, and evaluate after caching to avoid re-evaluating cached responses. Discuss how layers might interact, e.g., cache keys must account for redaction.
Use feature flags to roll out each layer gradually, with metrics for cache hit rate, redaction accuracy, and evaluation scores. Start with a canary deployment and monitor for regressions.
Acknowledge trade-offs: caching may reduce freshness, redaction may impact response quality, and evaluation adds cost. Mention alternatives like sidecar proxies or service mesh for cross-cutting concerns.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.