Assessment of a candidates practical proficiency across the technology stack and tools relevant to their role. This includes the ability to list and explain hands on experience with programming languages, frameworks, libraries, cloud platforms, data and machine learning tooling, analytics and visualization tools, and design and prototyping software. Candidates should demonstrate depth not just familiarity by describing specific problems they solved with each tool, trade offs between alternatives, integration points, deployment and operational considerations, and examples of end to end workflows. The description covers developer and data scientist stacks such as Python and C plus plus, machine learning frameworks like TensorFlow and PyTorch, cloud providers such as Amazon Web Services, Google Cloud Platform and Microsoft Azure, as well as design tools and research tools such as Figma and Adobe Creative Suite. Interviewers may probe for evidence of hands on tasks, configuration and troubleshooting, performance or cost trade offs, versioning and collaboration practices, and how the candidate keeps skills current.
EasyTechnical
0 practiced
Describe how you set up logging and basic monitoring for a model training job on a single AWS EC2 instance. Which metrics do you log (training loss, throughput, GPU utilization, memory), which aggregation tools do you use (CloudWatch, ELK), and how do you configure alerts for failed runs, walltime overruns, or resource exhaustion?
MediumBehavioral
0 practiced
Tell me about a time you reduced cloud costs for model training or serving. Describe the steps you took (spot/preemptible instances, autoscaling, batching, right-sizing), how you measured and validated savings, trade-offs in reliability or latency, and how you got stakeholder buy-in.
HardSystem Design
0 practiced
Architect a production ML platform that supports distributed training across regions, experiment tracking, a model registry, a feature store, and low-latency global serving. Draw a high-level architecture, describe data and artifact flow, managed vs self-hosted choices for components, cross-region consistency guarantees, and failure modes and recovery plans.
MediumTechnical
0 practiced
Compare managed ML services (SageMaker, Vertex AI, Azure ML) with building a Kubernetes-based ML platform. Discuss trade-offs in cost, time-to-market, control, scaling, security, and team skills. Provide examples of projects or company contexts where you'd pick each approach.
HardTechnical
0 practiced
A deep model's 95th-percentile inference latency is 500ms; requirement is under 50ms. Propose a set of engineering and model changes (distillation, pruning, quantization, batching, caching, precomputed embeddings, hardware inference accelerators) to meet SLA. Discuss accuracy trade-offs, rollout steps, and how you'd validate business impact.
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