Start by clarifying requirements and constraints, then design a high-level architecture that abstracts GPU resources across on-prem and cloud. Focus on the scheduler, resource management, and job lifecycle, and discuss trade-offs in placement, data movement, and cost.
Pro tip: Emphasize that the scheduler should be pluggable and policy-driven, allowing different strategies (e.g., cost-optimized, latency-optimized) without major refactoring. Also, mention that you'd start with a simple heuristic and iterate based on metrics.
Ask about scale, job types, latency requirements, data locality, security, and budget constraints. Understand the mix of on-prem and cloud GPUs and any compliance needs.
Outline components: a global scheduler, resource managers for each environment, a job queue, and a monitoring system. Describe how they interact via APIs.
Explain how jobs are matched to GPUs based on policies (e.g., cost, data locality, availability). Discuss preemption, gang scheduling, and handling failures.
Address data transfer between on-prem and cloud, caching, and network latency. Mention techniques like data staging and checkpointing.
Discuss trade-offs: cost vs. performance, complexity vs. flexibility. Explain how the system scales and handles bursts.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went through the four endpoints pretty quickly.
Start by clarifying the system's requirements and constraints, then propose a RESTful API design with clear resource-oriented endpoints. Walk through each operation (submit, status, list, cancel) with HTTP methods, paths, request/response schemas, and status codes, while discussing trade-offs and scalability considerations.
Pro tip: Demonstrate maturity by discussing idempotency for job submission (e.g., using client-provided idempotency keys) and asynchronous processing patterns, which are critical for reliable job systems.
Ask about expected scale, authentication, job types, and whether the API is public or internal. State assumptions to guide your design.
Identify the primary resource (e.g., jobs) and design RESTful endpoints for each operation: POST /jobs, GET /jobs/{id}, GET /jobs, DELETE /jobs/{id}.
For each endpoint, describe HTTP method, path, headers, request body, response body, and status codes. Include examples for clarity.
Discuss idempotency, pagination, filtering, rate limiting, error handling, and asynchronous job processing. Mention how to handle cancellation of running jobs.
Recap the API design and suggest possible extensions like webhooks, batch operations, or versioning, showing forward-thinking.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked through a jobs table with status, resource spec, placement info, and timestamps.
Start by clarifying the core entities and their relationships, then propose a schema that balances normalization for integrity with denormalization for query performance. Discuss how you would handle allocation logic, including constraints and concurrency, and consider scalability and access patterns.
Pro tip: Mention that you would first identify the most frequent queries and design indexes accordingly, as data modeling should be driven by access patterns, not just entity relationships.
Define the main entities: users, jobs, and resource allocations. Determine cardinality (e.g., one job has many allocations, one user can have many allocations) and any hierarchical or many-to-many relationships.
Propose tables for users, jobs, and allocations with primary keys, foreign keys, and essential attributes. For allocations, include fields like user_id, job_id, resource_type, quantity, start_time, end_time, and status.
Discuss how to enforce constraints such as preventing double-booking of resources, ensuring allocations don't exceed capacity, and handling time-based validity. Mention using database constraints or application-level checks.
Identify common queries (e.g., 'get all allocations for a user', 'find available resources for a job') and propose indexes, materialized views, or denormalization to optimize performance.
Address how the model would scale (e.g., sharding by user_id or job_id) and how to handle schema changes, soft deletes, and audit trails.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Node failure was easier to reason about: heartbeat from the worker, timeout triggers a reschedule.
Structure your answer by separating node-level and cloud API-level failures, then explain how you'd detect, mitigate, and recover from each. Emphasize idempotency, retries with backoff, and graceful degradation to keep jobs running reliably.
Pro tip: Mention that you'd design for failure by assuming nodes and APIs will fail, and that you'd use circuit breakers and dead-letter queues to prevent cascading failures. Also highlight the importance of observability and chaos testing to validate resilience.
Distinguish between node-level failures (e.g., crashes, resource exhaustion) and cloud provider API failures (e.g., throttling, timeouts, service outages).
Ensure job operations are idempotent so retries don't cause duplicate work. Implement retry logic with exponential backoff and jitter for transient API errors.
Use bulkheads to isolate failures, circuit breakers to stop calling failing APIs, and fallback mechanisms (e.g., cached data, alternate regions) to maintain progress.
Set up comprehensive monitoring for node health and API error rates, with alerts to detect and respond to failures quickly.
Regularly test failure scenarios via chaos engineering and game days, and refine strategies based on post-mortems and metrics.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Said quota enforcement at submission time with a soft check, then a hard check at scheduling time to handle race conditions.
Start by clarifying the requirements: what does 'fairness' mean here (equal share, weighted by priority, or max-min fairness)? Then outline a multi-layered approach: admission control, scheduling, and enforcement. Emphasize that quotas must be enforced at multiple levels (tenant, team, user) with observability and graceful degradation.
Pro tip: Mention that fairness is not just about quotas but also about preventing starvation and ensuring preemption or borrowing policies. Show you understand the trade-offs between strict isolation and utilization.
Ask questions to understand what fairness means in this context: equal access, weighted by team priority, or max-min fairness? Also clarify the scope: per-user, per-team, per-tenant, and how quotas are set (static or dynamic).
Propose a tree structure: tenant -> team -> user, with quotas at each level. Use a token bucket or leaky bucket for rate limiting, and consider borrowing unused capacity from parent or siblings.
At request time, check if the user/team/tenant has available quota. If not, either reject, queue, or preempt lower-priority jobs. Use a scheduler that enforces fairness (e.g., weighted fair queuing, DRF) and supports preemption.
Enforce quotas at the GPU allocation layer (e.g., Kubernetes device plugin, custom scheduler). Emit metrics for usage, quota violations, and fairness (e.g., Jain's fairness index). Set up alerts for abuse.
Discuss how to handle bursty workloads, quota changes, and failures. Consider a feedback loop to adjust quotas dynamically based on demand and priority.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Container registry for code, object storage for training data.
Start by clarifying the scale and constraints (e.g., dataset size, training frequency, compute environment), then propose a layered storage architecture that separates code, data, and artifacts. Explain how each layer is versioned, cached, and accessed by compute nodes, emphasizing trade-offs between performance, cost, and complexity.
Pro tip: Mention that code and data should be treated as immutable, versioned artifacts, and that caching strategies (e.g., node-local SSD, distributed cache) are critical to avoid I/O bottlenecks at scale.
Ask about dataset size, update frequency, compute node types, network topology, and security/compliance needs to tailor the solution.
Propose separate storage for code (e.g., Git, container registry), training data (e.g., object store like S3, HDFS), and artifacts (e.g., model checkpoints).
Explain how compute nodes fetch code (e.g., container images, git clone) and data (e.g., mount distributed filesystem, download from object store, use data loader with caching).
Discuss caching (node-local, distributed), data sharding, prefetching, and network optimizations to reduce latency and avoid bottlenecks.
Describe how to version code and data (e.g., Git SHA, dataset versioning), enforce access controls, and enable reproducibility.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Metrics pipeline from workers to a time-series store, job logs streamed to object storage and tailed via the status API.
Start by clarifying the system's components and user-facing goals, then outline a layered observability stack covering metrics, logs, and traces. Explain how you would expose relevant data to users through dashboards, alerts, and self-service tools, emphasizing actionable insights.
Pro tip: Frame observability as a product feature: focus on what users need to know to trust and operate the system, not just on collecting data. Mention SLOs and error budgets to show you connect reliability to business value.
Ask questions to understand the system architecture, critical user journeys, and who the users are (e.g., internal engineers, external customers). Identify what metrics and logs would be most valuable to them.
Propose tools for metrics (e.g., Prometheus), logs (e.g., ELK), and traces (e.g., Jaeger), ensuring they integrate with the system. Discuss instrumentation, data collection, and storage considerations.
Decide which metrics (e.g., latency, error rates, throughput) and logs (e.g., request IDs, error details) to surface. Plan user-facing dashboards, APIs, or embedded views that present this data clearly.
Set up alerts based on SLOs and error budgets, and provide users with self-service querying and filtering capabilities. Ensure alerts are actionable and routed to the right teams.
Establish processes for reviewing observability coverage, managing costs, and evolving the setup as the system grows. Include data retention policies and access controls.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.