This is the core question and it's deceptively broad.
Start by clarifying requirements and constraints (e.g., expected load, latency, video length, cost). Then design a scalable, asynchronous pipeline with a job queue, status tracking, and storage, treating the model as a black box. Emphasize trade-offs and failure handling.
Pro tip: Show awareness of cost and resource management: GPU inference is expensive, so implement priority queues, batching, and auto-scaling to optimize utilization. Also, discuss idempotency and exactly-once processing to avoid duplicate jobs.
Ask about expected request rate, video length, resolution, latency SLAs, and budget. This shapes the architecture and trade-offs.
Define a RESTful endpoint (e.g., POST /videos) that accepts a prompt and parameters, returns a job ID, and validates input. Use idempotency keys to handle retries.
Use a distributed message queue (e.g., Kafka, SQS) to decouple submission from processing. Workers pull jobs, call the black-box model, and handle retries with exponential backoff.
Store job metadata in a database (e.g., DynamoDB, PostgreSQL). Provide a GET /videos/{id} endpoint for polling, and optionally webhooks or WebSocket for push notifications.
Store generated videos in object storage (e.g., S3) with a CDN for fast retrieval. Return pre-signed URLs or stream via an API, and implement lifecycle policies for cleanup.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where the conversation got interesting.
Frame the problem as a queueing and scheduling challenge: GPU capacity is fixed, so you must manage demand through admission control, prioritization, and batching. Then discuss how to maximize utilization via continuous batching, preemption, and graceful degradation, while ensuring no requests are dropped by using queues and backpressure.
Pro tip: Emphasize that 'no dropped requests' doesn't mean all requests get equal treatment—use SLAs and priorities to shed load intelligently. Also, mention that utilization and latency are trade-offs; sometimes you intentionally keep utilization below 100% to absorb bursts.
Understand the nature of traffic bursts: are they predictable (e.g., daily peaks) or random? What are the latency SLAs for different request types? This informs the strategy.
Use a queue to buffer incoming requests during bursts. Apply admission control to reject or defer requests that exceed capacity, but ensure no requests are dropped by persisting them or returning a 'try later' response.
Employ continuous batching, dynamic batching, and preemption to keep GPUs busy. Prioritize requests based on SLAs and use techniques like chunked prefilling to interleave long and short jobs.
If possible, add more GPUs or use a mix of GPU types. Offload non-GPU work to CPUs and use spot instances for non-critical workloads to free up capacity.
Continuously monitor utilization, queue lengths, and latency. Autoscale the number of workers (if using a cluster) and implement graceful degradation (e.g., lower quality of service) to handle extreme bursts without dropping requests.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by contrasting the scaling characteristics of GPU-bound inference with traditional CPU-based services, emphasizing cold start latency, memory constraints, and cost. Then propose a multi-layered solution that combines predictive scaling, request queueing, and efficient batching to handle load spikes without over-provisioning.
Pro tip: Highlight that GPU instances often take minutes to become ready due to model loading and CUDA initialization, so autoscaling must be proactive rather than reactive. Also mention that keeping a warm pool of GPUs can be cost-effective if you leverage spot instances and intelligent scheduling.
Describe how traditional autoscaling relies on quick startup and horizontal scaling of stateless services, but GPU inference has long cold starts, high memory footprint, and expensive instance types.
Mention specific metrics like model load times (seconds to minutes), GPU memory limits that prevent multiple models per GPU, and the cost of idle GPUs.
Outline solutions such as predictive scaling based on historical traffic patterns, maintaining a warm pool of pre-loaded GPUs, and using request queueing with backpressure.
Discuss techniques like dynamic batching, model quantization, and multi-model serving to maximize throughput per GPU.
Acknowledge trade-offs between latency and cost, and emphasize the need for robust monitoring and alerting to adjust scaling policies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Pretty standard storage design question in this context.
Start by clarifying requirements like video size, generation rate, and access patterns, then propose a tiered storage architecture (hot/warm/cold) with object storage as the backbone. Discuss CDN integration for global low-latency delivery, and outline retention policies based on cost, compliance, and user expectations.
Pro tip: Emphasize that retention policies should be configurable per customer or use case, and mention the importance of lifecycle rules to automatically transition or delete data, showing you think about operational efficiency and cost.
Ask about video size, generation rate, access frequency, geographic distribution, and any compliance or retention requirements to scope the problem.
Propose a tiered storage approach: hot storage (e.g., SSD-backed object storage) for recent or frequently accessed videos, warm storage (e.g., standard object storage) for less frequent access, and cold storage (e.g., archival) for long-term retention.
Use a CDN to cache videos at edge locations for low-latency global delivery. Discuss cache invalidation strategies, signed URLs for security, and adaptive bitrate streaming if applicable.
Outline retention based on video age, user tier, and compliance. Use lifecycle policies to automatically delete or transition videos to cheaper storage. Consider soft deletes and versioning for recovery.
Discuss trade-offs between cost, latency, and durability. Mention monitoring storage usage, access patterns, and CDN hit rates to optimize continuously.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by framing the problem: long-running, expensive jobs (like video generation) must be resilient to mid-job failures. Then describe a layered strategy: detect failures, classify them (transient vs. permanent), and apply appropriate retry semantics with idempotency guarantees. Emphasize checkpointing, idempotency keys, and monitoring to ensure correctness and efficiency.
Pro tip: Show that you think about the cost of retries—both in compute and user experience—and that you design for graceful degradation, not just correctness. Mention that you'd log enough context to debug the root cause without compromising idempotency.
Explain how you detect a mid-job failure (e.g., heartbeat, timeout, error codes) and classify it as transient (e.g., node crash) or permanent (e.g., invalid input). This determines whether to retry.
Describe your retry policy: exponential backoff with jitter, max attempts, and fallback to different hardware or region. For transient failures, retry; for permanent, fail fast and alert.
Explain how you make operations idempotent: use idempotency keys for each job, checkpoint intermediate results (e.g., frames generated), and ensure that re-executing a step doesn't duplicate side effects.
Detail how you persist progress (e.g., save generated frames to durable storage) so that on retry, the job resumes from the last checkpoint instead of starting over, saving time and cost.
Discuss monitoring retry rates, failure causes, and idempotency violations. Use this data to improve the system, such as tuning retry parameters or adding redundancy.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Queue depth, p50/p99 generation latency, GPU utilization percentage, and cost per job.
Start by clarifying the system's purpose and critical user journeys, then propose a layered observability strategy covering infrastructure, application, and business metrics. Define what 'healthy' looks like with specific SLOs and thresholds, and contrast with degraded states using concrete signals and potential causes.
Pro tip: Tie every metric to a user-facing impact and propose automated alerting with runbooks; this shows you think beyond dashboards to actionable reliability.
Ask clarifying questions about the system's architecture, scale, and key user journeys to tailor your observability plan.
Outline metrics across infrastructure (CPU, memory), application (latency, error rates), and business (user engagement, conversion) layers.
Propose specific SLOs (e.g., 99.9% availability, p95 latency < 200ms) and define what constitutes a healthy system versus degraded.
Explain how degraded states manifest (e.g., increased latency, error spikes) and what tools (tracing, logging) help diagnose root causes.
Discuss alerting thresholds, escalation policies, and runbooks to ensure timely detection and resolution of issues.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.