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.

HardSystem Design

65 practiced

Design an end to end MLOps architecture to support daily training of many models on petabyte scale datasets. Include components for data ingestion, feature store, distributed training cluster autoscaling, experiment tracking, model registry, artifact storage, serving, monitoring, and rollback, and specify which cloud managed or open source tools you would use and why.

EasyTechnical

77 practiced

Describe practical guidelines for deciding when to use CPUs, GPUs, or TPUs for both training and inference. Discuss multi GPU training, mixed precision, memory bottlenecks, batch sizes, cost trade offs and when specialized hardware like TPUs or inference accelerators are a good fit.

EasyTechnical

63 practiced

Explain the core stages of a CI CD pipeline for an ML model project, including steps for unit testing, data validation, model training or building, model evaluation, artifact publishing, container build, deployment to staging, and gated promotion to production. Mention what should be automated versus manual approvals.

MediumSystem Design

79 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.

MediumTechnical

84 practiced

Explain how to benchmark and compare inference throughput and latency across different instance types and runtimes. Describe tooling for load generation, metrics to capture, how to ensure test reproducibility, and how to isolate variables such as container runtime, CPU pinning, and network overhead.

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