← 🗄️ Data & SQL Engineering
Core
Data Quality and Validation
A deployment lives or dies on the customer's data, and that data is worse than their sample suggested. The job is to build automated quality gates (schema, null, range, uniqueness, freshness) at the boundary, quarantine bad records instead of failing the whole batch, and alert on the rate so a Tuesday-shaped degradation surfaces before a dashboard goes wrong. This is the difference between a pipeline that fails loudly and one that lies quietly.
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RELATED CONCEPTS
LESSONS THAT TEACH THIS
Foundations of Forward Deployed EngineeringTest the stated request against the work people actually do28 min→Foundations of Forward Deployed EngineeringSpecify the ownership, trust, network and meaning at each boundary28 min→Foundations of Forward Deployed EngineeringTest replay, slow dependencies and changing data before adding load30 min→
PRACTICE THIS IN REAL QUESTIONS
Machine Learning & Data ScienceExplain k-fold cross-validation, and when would you refuse to use it?→MLOps & ML EngineeringHow did you ensure quality and testing on top of your MLOps pipeline?→MLOps & ML EngineeringHow do you ensure reproducibility in ML workflows?→System Design & Production EngineeringDesign observability for an LLM application. What do you log, trace, and alert on, and how is it different from normal services?→MLOps & ML EngineeringHow would you implement an MLOps pipeline on AWS using SageMaker, CodePipeline, and Lambda?→MLOps & ML EngineeringExplain MLOps on Azure, how do Azure DevOps and Azure ML Pipelines fit together?→