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About this interview
A technical interview on Data Versioning and Pipelines, pitched at the medium level. A voice AI interviewer leads the conversation, adapts its questions to your answers, keeps you on topic, and afterward gives you honest, specific feedback on where you were strong and where to improve. Expect roughly 30 minutes.
What you'll be assessed on
Explain why large ML datasets require specialised versioning tools beyond standard git
Describe the DVC model: tracking data by hash, remote storage, and pipeline stages
Discuss DataOps principles: data validation, lineage tracking, and schema contracts
Identify failure modes that arise when training and serving data are not version-locked
Topics covered
Data Versioning FundamentalsDVC MechanicsML PipelinesDataOps PrinciplesData Quality and Failure Modes
A few sample questions
Just examples to set expectations - the real interview has many more and adapts to your responses.
“Why can't teams just use standard git to version large ML datasets the same way they version code?
“What is the difference between data quality and data validation? Can you have one without the other?
“Describe what metadata management means for ML data pipelines. What metadata should you capture at each stage and who consumes it?