Cognitiv interview questions

Showing 1–3 of 3 results

• Design a Real-Time Feature StoreMedium

  This question evaluates a candidate's ability to design real-time feature store architectures, encompassing competencies in low-latency serving, point-in-time correctness for offline training, streaming ingestion, consistency between online and offline features, handling backfills and late events, and scalable monitoring of data quality and reliability. It is commonly asked to assess systems-design and data-engineering judgment for production-grade ML infrastructure, falls under the ML system design and data engineering domain, and tests both conceptual understanding of trade-offs (consistency, latency) and practical application skills for APIs, storage, and operational design.

  CognitivMachine Learning EngineerML System DesignTechnical Screen
• Maintain a Streaming MedianMedium

  This question evaluates understanding of streaming algorithms and dynamic data structures for maintaining order statistics, along with analysis of time complexity and memory trade-offs.

  CognitivMachine Learning EngineerCoding & AlgorithmsTechnical Screen
• Reason About Duplicate Data and Scaling in SparkHard

  This problem tests the ability to design an idempotent, scalable data deduplication pipeline in Apache Spark while considering distributed systems constraints like data skew and state management.

  CognitivSoftware EngineerSoftware Engineering FundamentalsTechnical Screen