Start by clarifying requirements (batch size, latency, failure handling) and then present a high-level architecture with clear separation of concerns: ingest API, scheduler, workers, status store, and notifier. Walk through each component's responsibility, data flow, and trade-offs, emphasizing scalability, reliability, and idempotency.
Pro tip: Emphasize idempotency and exactly-once processing for job submissions and notifications, as duplicate jobs or notifications can be costly in production. Also, discuss how you would handle partial failures and retries with exponential backoff.
Ask about expected batch size, job duration, latency requirements, failure tolerance, and whether jobs are idempotent. This shapes the design choices.
Define an API endpoint to accept batch job parameters, validate them, and persist the batch and individual jobs in a durable store (e.g., DynamoDB) with statuses.
Use a scheduler (e.g., SQS + Lambda or Step Functions) to pick up pending jobs, submit them to the external async API, and record the external job ID. Ensure idempotency and retries.
Poll or receive callbacks from the external API to update job statuses. Use a counter or aggregation to detect when all jobs in a batch are complete.
Once all jobs complete, send a notification (e.g., SNS, email) to the user. Optionally, clean up or archive batch data.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked about exponential backoff with jitter, which landed well.
Start by distinguishing between job submission (write operation) and status check (read operation), then outline a retry strategy with exponential backoff and jitter for each. Emphasize idempotency for submissions via unique keys and for status checks via caching or deduplication, and discuss trade-offs like latency vs. reliability.
Pro tip: Mention that you would use client-generated idempotency keys for job submissions and store them with a TTL to prevent duplicate processing, and for status checks, consider using a conditional GET with ETag or caching to avoid unnecessary calls. Also, highlight the importance of monitoring retry metrics to tune backoff parameters.
Explain that job submission is a non-idempotent write that requires idempotency keys, while status check is a read that can be retried safely but should be optimized to avoid excessive calls.
Describe using exponential backoff with full jitter to avoid thundering herd, and set a maximum retry limit and timeout. For status checks, use a shorter backoff and consider polling with increasing intervals.
Generate a unique idempotency key per job submission (e.g., UUID) and include it in the request header. The external API should use this key to deduplicate; on the client side, store the key and response to handle retries.
Status checks are naturally idempotent, but to avoid redundant calls, cache responses with a short TTL or use conditional requests (e.g., If-None-Match with ETag) to only fetch when status changes.
Talk about balancing retry aggressiveness with API rate limits and cost. Mention logging retry attempts, success rates, and latency to tune backoff parameters and detect issues.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Blanked for a second on stuck job detection.
Start by clarifying the batch job's requirements (e.g., SLAs, data volume) and then describe a layered detection and handling strategy: monitoring for stuck jobs, timeout mechanisms, and partial failure recovery. Emphasize idempotency, checkpointing, and dead-letter queues to ensure robustness and fault tolerance.
Pro tip: Tie your answer to Amazon's leadership principles: 'Dive Deep' by instrumenting metrics and logs, and 'Deliver Results' by designing for automatic recovery and minimal manual intervention.
Ask about batch size, SLAs, data criticality, and existing infrastructure to tailor your approach. This shows you understand the context before diving into solutions.
Use heartbeats, progress tracking, and timeouts to detect stuck jobs. Monitor metrics like job duration, error rates, and resource utilization with alerts.
Set per-task and overall job timeouts; on timeout, kill and retry with exponential backoff. Use dead-letter queues for repeatedly failing tasks.
Design idempotent tasks and checkpointing to resume from last successful point. Isolate failures to prevent whole-batch failure and use compensating transactions if needed.
Log detailed errors, emit metrics, and create dashboards for visibility. Automate recovery where possible and provide manual override for critical failures.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements and constraints, then propose a scalable architecture that decouples job submission, processing, and status updates. Compare polling vs. event-driven callback models, highlighting trade-offs in latency, cost, and complexity, and recommend a hybrid approach based on the specific use case.
Pro tip: Emphasize idempotency and failure handling in both models, as external APIs can be unreliable. Also, mention that at Amazon's scale, event-driven is often preferred for real-time updates, but polling can be simpler and more robust for batch processing.
Ask about job volume, latency requirements, external API rate limits, and reliability guarantees to scope the problem.
Propose a decoupled system using queues (e.g., SQS) and auto-scaling workers (e.g., EC2, Lambda) to handle millions of jobs.
Discuss trade-offs: polling is simpler but can be inefficient; event-driven is real-time but requires webhook support and idempotency.
Choose a model based on requirements, or propose a hybrid approach, and explain how it meets scalability and reliability needs.
Cover monitoring, retries, dead-letter queues, and cost optimization to ensure production readiness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Deduplication I tied back to the idempotency key idea from earlier.
Start by clarifying the system's context and requirements, such as the types of jobs, expected scale, and latency constraints. Then propose a deduplication strategy using idempotency keys and a distributed store, and a cancellation mechanism with cooperative checks and state management. Discuss trade-offs and how to handle edge cases like race conditions and partial failures.
Pro tip: Emphasize idempotency and exactly-once semantics, as these are critical in distributed systems like Amazon's. Also, mention how you would monitor and alert on deduplication and cancellation metrics to ensure reliability.
Ask about job types, scale, latency, and consistency requirements to tailor your solution. Confirm whether jobs are batch or streaming, and if cancellation needs to be immediate or can be eventual.
Propose using a unique idempotency key per job, stored in a distributed cache or database with TTL. Discuss how to handle duplicate submissions and ensure exactly-once processing.
Outline a cancellation API that sets a cancellation flag in a shared store, and workers periodically check this flag. For long-running jobs, implement cooperative cancellation with checkpoints.
Explain how to handle concurrent deduplication and cancellation requests, such as using atomic operations or distributed locks. Discuss recovery from partial failures and ensuring state consistency.
Compare different approaches (e.g., strong vs. eventual consistency) and their impact on performance and complexity. Mention monitoring deduplication rates and cancellation success to detect issues.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Rattled off the standard stuff: job latency histograms, failure rates per batch, queue depth metrics, dead letter queue alerting.
Start by clarifying the system's components (data pipeline, training, inference, serving) and then propose a layered observability strategy covering metrics, logs, and traces. Emphasize proactive alerting on both system health and model performance, and tie everything back to business impact and Amazon's customer obsession.
Pro tip: Differentiate between monitoring the system (latency, errors) and monitoring the model (drift, bias, quality). Show you understand that ML systems require both, and that alerting thresholds should be based on business SLAs, not just technical metrics.
Identify key user journeys and business outcomes, then define Service Level Indicators (SLIs) and Objectives (SLOs) for each component (e.g., prediction latency <100ms, model accuracy >95%).
Collect system metrics (CPU, memory, throughput), application logs (errors, request IDs), and distributed traces to enable end-to-end visibility. Use tools like CloudWatch, X-Ray, and Prometheus.
Track model-specific metrics: prediction drift, feature drift, data quality issues, and model performance degradation. Set up automated retraining triggers when thresholds are breached.
Create actionable alerts with clear severity levels, routing to on-call engineers. Avoid alert fatigue by using composite alerts and anomaly detection. Include runbooks for common issues.
Create dashboards for different stakeholders (engineers, data scientists, product managers) showing real-time and historical trends. Incorporate user feedback and A/B test results to continuously improve the system.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.