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.

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.

HardSystem Design

0 practiced

Design an automatic hyperparameter tuning system that executes distributed experiments on spot instances while controlling total budget and ensuring reproducibility. Discuss scheduler choices such as population based methods, Bayesian optimization, or ASHA, checkpointing and resuming trials, metadata to store, and how to compare trials with confidence intervals.

EasyTechnical

0 practiced

Explain infrastructure as code and compare Terraform, CloudFormation, and Pulumi for provisioning the cloud resources used by ML workloads. For which ML platform components would you insist on IaC and why, and how do you manage drift and environment parity between dev, staging, and prod?

HardTechnical

0 practiced

Compare deployment strategies for model updates including blue green, canary, shadowing, gradual traffic shifting, and gated promotion. For each approach explain rollback conditions, observability requirements, complexity and cost implications, and a recommended promotion criteria for production ML models.

HardSystem Design

0 practiced

Design a scalable data validation and schema enforcement system that runs as part of data pipelines to stop bad data from entering training or serving. Include how to express expectations, where to run checks (streaming vs batch), quarantine or rollback actions, alerting, and the user experience for data engineers to resolve failures.

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