AI Load Planning Specialist Interview Questions

Distinguish adding more powerful machines (vertical) from adding more instances of the same machine (horizontal), and mention which is more common for GPU workloads.

Explain how batching improves GPU utilization by processing multiple inputs in parallel, reducing the cost per inference.

Describe automated deployment, scaling, and management of containerized AI applications.

Define it as the proportion of time GPU cores are actively working; low utilization indicates idle, costly resources.

Define IaC as managing infrastructure through code files for reproducibility; mention Terraform or CloudFormation.

Discuss metrics to scale on (requests per second, queue length), cooldown periods, and predictive scaling based on historical patterns.

Contrast managed service (simpler ops, less control) vs. self-managed (more flexibility, higher ops burden). Mention cost, customization, and team expertise.

Explain reducing model precision (e.g., FP32 to INT8) to lower memory footprint and increase throughput, impacting hardware choice and scaling.

Include request latency (p50, p95, p99), GPU memory usage, GPU utilization, error rates, queue depth, and throughput (tokens/sec).

Describe a rollback strategy, A/B testing with canary deployments, and investigating the root cause (model change, code bug, data shift).

Define cold start as latency from initializing a new container/instance; mitigate with provisioned concurrency, keep-alive pings, or pre-warming.

Consider GPU memory, GPU compute power (FLOPS), network bandwidth, cost per hour, and availability.

Discuss using a key based on input prompt and model version, setting TTLs, and handling cache invalidation for updated models.

Explain its role in storing/retrieving context; challenges include scaling read-heavy workloads, embedding compute, and cost of managed services.

Throughput = total work done per time; latency = time for one request. Prioritize latency for user-facing apps, throughput for batch processing.

Describe a hybrid approach: a baseline of reserved instances or committed use discounts, with auto-scaling on spot/preemptible instances for peaks, and a queue to buffer requests.

Discuss deploying model replicas in key regions, using a global load balancer (e.g., AWS Global Accelerator), and ensuring data pipelines comply with regional regulations like GDPR.

Explain techniques that reduce the number of steps to generate a token, thus increasing effective throughput. It makes resource planning more complex as utilization patterns change.

Describe load testing with synthetic traffic, defining SLOs based on business requirements, starting with a conservative over-provision, and monitoring closely post-launch.

Compare total cost of ownership (TCO), operational overhead, flexibility for custom optimizations, vendor lock-in, and access to cutting-edge features.

Investigate potential bottlenecks: memory bandwidth saturation, inefficient data loading, kernel launch overhead, or issues in the pre/post-processing pipeline rather than the GPU itself.

Mention optimizing model efficiency, scheduling compute-intensive tasks during periods of low carbon-intensity energy, using more efficient hardware, and carbon-aware cloud regions.

Describe using feature flags, splitting traffic at the load balancer level, monitoring both new and old model metrics simultaneously, and having a clear rollback trigger.

Discuss running a benchmark on a sample dataset to measure tokens per second per dollar, extrapolating to expected traffic patterns, and factoring in overhead from redundancy, networking, and storage.

Explain techniques like request prioritization, returning degraded responses (e.g., shorter answers), or queuing non-critical requests, all while protecting the core service.

Outline steps: estimate average requests per user, define latency SLO, benchmark model on candidate hardware, design a scalable architecture (likely on Kubernetes with auto-scaling), and estimate baseline cost.

Start with monitoring: check for inefficient code (e.g., processing entire user history), lack of caching, over-provisioned instances, or data skew causing some models to be much slower. Analyze cost allocation tags.

Focus on model optimization (quantization, distillation), choice of serving framework (e.g., Triton with dynamic batching), infrastructure (high-frequency CPUs or optimized GPUs), and rigorous load testing.

Propose a mixed instance strategy: a small baseline of reserved or on-demand instances for reliability, with spot instances for scaling. Implement robust checkpointing and fast instance recycling in the orchestration layer.

Design a routing layer (could be ML-based or rule-based) that directs traffic. Serve each model on appropriately sized hardware. This is a classic 'cascading' or 'multi-model' serving architecture.

Prioritize: 1) Right-sizing and purchasing reserved instances/savings plans. 2) Implementing aggressive caching and batching optimizations. 3) Reviewing and decommissioning underutilized models/endpoints.

Immediate: Roll back the deployment. Long-term: Work with the scientist to optimize the model (quantization, pruning), evaluate newer GPU instances with more memory, or design a sharding strategy for the model.

Plan for: pre-scaling infrastructure to handle the initial surge, implementing a robust queue with backpressure, having a detailed runbook for the ops team, and setting up real-time monitoring war rooms.

Implement rate limiting per API key/IP, analyze traffic for bot signatures, and use a web application firewall (WAF). This protects your infrastructure and ensures fair usage for legitimate users.

Research the model's architecture (Mixture of Experts), understand its memory and compute profile, evaluate if current serving frameworks (like vLLM) support it, and potentially adjust auto-scaling metrics based on its different performance characteristics.

Discuss the multi-step, agentic nature: each chain step may call different models/tools. Load planning must account for variable latency, failure points in the chain, and resource needs for the orchestrator itself.

Use the tools to log metrics from load tests and production (latency, cost per experiment). Correlate model architecture/hyperparameter choices with serving performance to inform future model development.

Shift from scaling compute centrally to managing a fleet of diverse, constrained devices. Focus on over-the-air update efficiency, offline functionality, and aggregating telemetry from thousands of endpoints.

Detail using modules for reusable components (e.g., 'model_endpoint'), workspaces for environment separation (dev, prod), and managing state files. Mention integrating with CI/CD for automated plan/apply.

Explain instrumenting each service to propagate context, exporting traces to a backend like Jaeger or Grafana Tempo, and creating dashboards that show the full request flow and latency breakdown.

Describe defining input payloads, running tests with varying concurrency, measuring latency, throughput, and memory usage, and analyzing the results to find the optimal batch size and instance type.

Explain the event-driven, scalable nature but highlight the cold start problem, execution time limits, and memory constraints that make it unsuitable for many large generative models, but good for small, fast models.

The feature store provides low-latency access to pre-computed features. It reduces load on the model by simplifying pre-processing, but introduces its own scaling and availability requirements as a critical dependency.

Route a copy of live traffic to the new model but don't serve its responses to users. Compare the new model's metrics (latency, output) against the live model in a staging environment.

Use HPA to scale the number of pods based on CPU/memory or custom metrics (like GPU utilization). Use VPA to right-size the resource requests of individual pods based on historical usage. They solve different scaling dimensions.

Look for ability to use analogies, focus on business impact (user experience, cost), and present clear trade-offs or alternative solutions.

Assess risk management, use of data-driven estimates, and the process for setting up safeguards or monitoring to validate the decision.

Look for a structured approach: audit, analysis, hypothesis, pilot implementation, measurement, and rollout. Highlight collaboration with finance or engineering teams.

Seek mention of specific resources (e.g., following key researchers on Twitter/X, reading Arxiv Sanity, attending conferences like MLOps Community, hands-on experimentation with new tools).

Look for communication skills, ability to translate requirements, and examples of building consensus around a technical solution that meets multiple constraints.