Technical Foundation and Self Assessment Questions

Covers baseline technical knowledge and the candidate's ability to honestly assess and communicate their technical strengths and weaknesses. Topics include fundamental infrastructure and networking concepts, operating system and protocol basics, core development and platform concepts relevant to the role, and the candidate's candid self evaluation of their depth in specific technologies. Interviewers use this to calibrate how technical the candidate is expected to be, identify areas for growth, and ensure alignment of expectations between product and engineering for collaboration.

MediumBehavioral

0 practiced

Behavioral/technical: Which cloud platform(s) have you used for data storage and compute (AWS, GCP, Azure, or others)? Pick one you are most experienced with and describe a specific project where you used its managed services end-to-end (storage, compute, orchestration), the architecture decisions you made, and one limitation you encountered.

EasyTechnical

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Define overfitting and underfitting in predictive modeling. Describe two regularization techniques and one validation strategy you would use to detect and reduce overfitting on a tabular dataset used for regression.

HardTechnical

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Explain differential privacy and how you would implement DP-SGD to train a deep learning model. Describe the role of gradient clipping, noise addition, the privacy parameter epsilon, and the practical trade-offs between model utility and privacy guarantees.

HardTechnical

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You operate on data containing personally identifiable information under GDPR. Describe a technical and operational design to enforce data access controls, audit logging, right-to-be-forgotten deletions, and explainability for models trained on this data. Include how you would implement lineage and deletion propagation to derived features and models.

HardTechnical

0 practiced

A matrix factorization training job for collaborative filtering is memory-bound on a single machine. Propose algorithmic and system-level strategies to reduce memory use and speed up training: discuss sparse representations, sharding the model across workers, streaming SGD, checkpointing, use of mixed precision, and any trade-offs involved.

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