Data Pipelines and Feature Platforms Questions

Designing and operating data pipelines and feature platforms involves engineering reliable, scalable systems that convert raw data into production ready features and deliver those features to both training and inference environments. Candidates should be able to discuss batch and streaming ingestion architectures, distributed processing approaches using systems such as Apache Spark and streaming engines, and orchestration patterns using workflow engines. Core topics include schema management and evolution, data validation and data quality monitoring, handling event time semantics and operational challenges such as late arriving data and data skew, stateful stream processing, windowing and watermarking, and strategies for idempotent and fault tolerant processing. The role of feature stores and feature platforms includes feature definition management, feature versioning, point in time correctness, consistency between training and serving, online low latency feature retrieval, offline materialization and backfilling, and trade offs between real time and offline computation. Feature engineering strategies, detection and mitigation of distribution shift, dataset versioning, metadata and discoverability, governance and compliance, and lineage and reproducibility are important areas. For senior and staff level candidates, design considerations expand to multi tenant platform architecture, platform application programming interfaces and onboarding, access control, resource management and cost optimization, scaling and partitioning strategies, caching and hot key mitigation, monitoring and observability including service level objectives, testing and continuous integration and continuous delivery for data pipelines, and operational practices for supporting hundreds of models across teams.

MediumTechnical

0 practiced

A downstream model needs to be retrained weekly, but the training job often fails due to inconsistent data schema from upstream ETL. Propose an approach combining schema registry, contract testing, and automated gating to reduce such failures. Include how to handle rollback and emergency fixes.

HardSystem Design

0 practiced

Describe how you would implement feature versioning in a feature platform so that models can reproducibly train on a specific set of feature definitions and materializations. Cover metadata, storage of historical feature values, APIs for retrieving versions, and how to handle deprecation of features.

MediumTechnical

0 practiced

Explain the difference between 'exactly-once' and 'at-least-once' processing guarantees in streaming systems. Give two practical strategies to achieve idempotent writes to an external feature store or online store when the upstream stream may re-deliver records.

HardSystem Design

0 practiced

You need to generate offline training datasets with consistent feature snapshots for thousands of model runs. Explain how you'd implement efficient materialization and storage (e.g., partitioning, columnar formats, compaction) to support fast access and cost-effective storage, and how you'd expose them to data scientists.

EasyTechnical

0 practiced

Define a feature store in the context of machine learning infrastructure. Explain the differences between a feature store, a traditional OLTP database, and a data warehouse, focusing on responsibilities such as feature definition, online serving, offline materialization, and point-in-time correctness. Include one short example of when a feature store would be preferable.

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