Technology Stack Knowledge Questions

Assess a candidate's practical and conceptual understanding of technology stacks, including major programming languages, application frameworks, databases, infrastructure, and supporting tools. Candidates should be able to explain common use cases and trade offs for languages such as Python, Java, Go, Rust, C plus plus, and JavaScript, including differences between compiled and interpreted languages, static and dynamic type systems, and performance characteristics. They should discuss application frameworks and libraries for frontend and backend development, common web stacks, service architectures such as monoliths and microservices, and application programming interfaces. Evaluate understanding of data storage options and trade offs between relational and non relational databases and the role of structured query language. Candidates should be familiar with cloud platforms such as Amazon Web Services, Google Cloud Platform, and Microsoft Azure, infrastructure components including containerization and orchestration tools such as Docker and Kubernetes, and development workflows including version control, continuous integration and continuous delivery pipelines, testing frameworks, automation, and infrastructure as code. Assess operational concerns such as logging, monitoring and observability, deployment strategies, scalability, reliability, fault tolerance, security considerations, and common failure modes and mitigations. Interviewers may probe both awareness of specific tools and the candidate's depth of hands on experience, ability to justify technology choices by evaluating trade offs, constraints, and risk, and willingness and ability to learn and evaluate new technologies rather than claiming mastery of everything.

MediumSystem Design

0 practiced

Propose an architecture for detecting data drift and concept drift in production models using open source components. Describe how to collect and store telemetry, compute drift metrics, define thresholds and alerts, and what automated actions the system could take such as triggering retraining or routing traffic away from degraded models.

EasyTechnical

0 practiced

Define observability for ML systems and explain the three pillars of observability: metrics, logs, and traces. Provide concrete examples of model specific observability signals such as prediction distributions, feature drift metrics, latency p50 and p99, and explain how to use these signals for alerts and diagnosis.

HardSystem Design

0 practiced

Design a Kubernetes operator at a high level that manages GPU training jobs, supports using spot instances with automatic retries and checkpointing, and can migrate running jobs if spot instances are preempted. Describe the custom resource definition fields, reconciliation logic, checkpoint storage choices, and failure recovery behavior.

EasyTechnical

0 practiced

For storing features and training data, compare relational databases and non relational stores in the context of ML workloads. Discuss trade offs in consistency, schema evolution, query patterns, indexing, joins, scalability, cost, and when to use a feature store versus a transactional database or object storage.

MediumTechnical

0 practiced

Describe the steps to convert a PyTorch model to ONNX and then optimize it with TensorRT for lower latency inference on NVIDIA GPUs. Include considerations such as dynamic vs static shapes, unsupported operators, calibration for int8 quantization, and numerical equivalence validation strategies.

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