← Anthropic Interview Insights
This is basically four interview questions duct-taped together.
Start by clarifying requirements and assumptions (e.g., read/write ratio, data size, consistency needs) to scope the design. Then walk through a high-level architecture covering multi-region deployment, API design, storage, caching, consistency, partitioning, failure handling, and canary rollout, emphasizing trade-offs at each layer. Conclude by discussing how ML-specific components (e.g., model serving, feature stores) integrate and meet latency/throughput goals.
Pro tip: Quantify the impact of your choices: e.g., 'Using eventual consistency with read-your-writes via session tokens reduces cross-region latency by X ms while meeting 99.9% of user expectations.' This shows you think in terms of measurable trade-offs, not just buzzwords.
Ask about read/write ratio, data volume, consistency requirements, SLA, and ML workload characteristics (e.g., model size, inference frequency). State assumptions explicitly to guide the design.
Define RESTful or gRPC APIs with versioning, pagination, and idempotency. Outline storage schema (e.g., user profiles, features, model metadata) and choose appropriate databases (e.g., Cassandra for writes, Redis for caching).
Deploy active-active across regions with global load balancing. Partition data by user ID or geography, use consistent hashing, and implement caching layers (CDN, application cache) to reduce latency and load.
Choose a consistency model (e.g., eventual with read-your-writes) and implement mechanisms like quorum reads/writes, conflict resolution, and circuit breakers. Plan for region failover, retries with backoff, and graceful degradation.
Use canary deployments with traffic shifting (e.g., 1% to new version), automated rollback on error/latency thresholds, and comprehensive monitoring (p95 latency, error rates, QPS) across regions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the system's purpose and scale (e.g., ML inference service with user queries and model updates). Then walk through a structured estimation: assume a user base, derive QPS, split read/write, project data growth, compute peak load, size instances, and estimate egress. Use round numbers and state assumptions clearly.
Pro tip: Anchor your estimates to known benchmarks (e.g., typical LLM inference latency, token sizes) and explicitly call out where you'd validate with real metrics. This shows you understand the difference between a rough plan and production reality.
Ask clarifying questions about the system (e.g., is it an ML training or inference service? What's the user base?). State your assumptions for user count, request size, and data retention.
Derive average QPS from daily active users and requests per user. Split into reads (e.g., inference requests) and writes (e.g., logging, model updates). For ML, reads often dominate (e.g., 100:1).
Calculate data generated per request (input + output tokens) and multiply by daily requests to get daily growth. Project over 12 months. Estimate peak load as a multiple of average (e.g., 3-5x) based on traffic patterns.
Determine required compute (e.g., GPU instances for inference) based on QPS and latency SLA. Estimate storage needs and network egress (e.g., data transferred to users) and compute monthly costs using cloud pricing.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I blanked for a second when they said queueing approximations.
Start by decomposing the end-to-end latency into its component service times (e.g., network, compute, I/O) and then apply queueing theory approximations (like M/M/1 or M/G/1) to model waiting times under load. Use the model to identify the bottleneck resource by comparing utilization and sensitivity to load, and validate with empirical measurements if possible.
Pro tip: Emphasize that the bottleneck is often not the component with the highest service time but the one with the highest utilization and variability; use Little's Law to connect concurrency, throughput, and latency.
Break down the request path into stages (e.g., client, network, load balancer, service, database) and estimate or measure the service time and variability at each stage.
Apply appropriate queueing models (e.g., M/M/1, M/M/c, M/G/1) to each stage, using arrival rate and service rate to compute waiting time and total latency.
Sum the service and waiting times across stages, accounting for parallelism and dependencies, to get the overall latency as a function of load.
Determine which stage has the highest utilization or contributes most to latency growth under load; this is the bottleneck. Use sensitivity analysis to confirm.
Compare model predictions with empirical measurements (e.g., load tests) and refine assumptions or model parameters as needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Rattled off the usual suspects: write batching, async fan-out for non-critical paths, circuit breakers at service boundaries.
Start by mapping each identified bottleneck to a concrete mitigation, explaining the trade-offs and expected impact. Then define SLOs that quantify the desired performance, and describe a load-testing plan that validates the mitigations under realistic traffic patterns. Emphasize iterative refinement based on test results.
Pro tip: Tie every mitigation to a measurable SLO and explain how you'd validate it with load tests—this shows you think in terms of end-to-end reliability, not just isolated fixes. Also, mention that you'd start with the highest-impact bottleneck first to deliver value quickly.
For each bottleneck, propose specific techniques (e.g., batching, async workflows, indexing, autoscaling, circuit breaking) and justify why they address the root cause. Discuss trade-offs such as latency vs. throughput or cost vs. reliability.
Specify measurable SLOs (e.g., p99 latency < 200ms, availability 99.9%, error rate < 0.1%) that reflect user expectations and business needs. Ensure they are achievable and tied to the mitigations.
Outline how you'd load-test: tools (e.g., Locust, JMeter), scenarios (peak load, spike, soak), metrics to collect, and how you'd simulate realistic traffic. Include validation of each mitigation under load.
Describe how you'd analyze load-test results, identify remaining gaps, and adjust mitigations or SLOs. Emphasize continuous improvement and monitoring in production.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.