Technical Tools and Stack Proficiency Questions

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.

EasyBehavioral

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How do you keep your ML tooling skills current? Provide a concrete learning plan, examples of resources you follow such as papers, repositories, newsletters, and a recent example of a tool or library you learned in the past year and how it changed a workflow.

MediumTechnical

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How would you set up monitoring and alerting for model performance degradation in production? Define specific metrics to collect, drift detection techniques, thresholds that trigger alerts, and a remediation playbook that includes investigation steps and possible rollbacks or retraining actions.

MediumTechnical

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Show or describe the steps to convert a PyTorch model to TorchScript and then export to ONNX. Include code patterns you would use, tests to confirm numerical equivalence, and strategies for handling unsupported ops during export.

MediumTechnical

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An Airflow DAG that retrains a model daily has intermittent failures due to S3 connection errors. Outline how you would diagnose the root cause and harden the DAG: include retries and backoff strategy, idempotency, alerting, and how to test the changes before rolling out.

HardSystem Design

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Design an enterprise experiment tracking and metadata architecture that supports many teams. Include a central metadata store, metadata schema for experiments and artifacts, access control, lineage linking datasets models and code, search and indexing capabilities, and recommendations for open-source versus managed solutions.

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