Data Validation, Leakage Prevention & Statistical Rigor Questions

Data validation and governance practices within data pipelines and analytics platforms, including schema validation, data quality checks, anomaly detection, lineage, and data quality metrics. Addresses leakage prevention in analytics and machine learning workflows (e.g., proper train/test separation, cross-validation strategies, and leakage risk mitigation) and emphasizes statistical rigor in analysis and modeling (experimental design, sampling, hypothesis testing, confidence intervals, and transparent reporting). Applicable to data engineering, analytics infrastructure, and ML-enabled products.

MediumTechnical

0 practiced

Sketch a Spark job (pseudocode) that computes Population Stability Index (PSI) between training and production numeric feature distributions at scale. Show how you handle nulls, define bins (e.g., quantile-based or fixed bins), and compute PSI per feature while minimizing shuffle and memory usage.

MediumSystem Design

0 practiced

You must capture data lineage and metadata across ELT jobs so feature ownership, freshness, and upstream changes are auditable. Describe a design using open standards (OpenLineage, DataHub, Amundsen) that supports: registering datasets, capturing job/column-level lineage, alerting on upstream schema/freshness failures, and using lineage to block model training if dependencies are stale or broken.

MediumTechnical

0 practiced

Create a lightweight validation schema in YAML or JSON and describe how you would integrate this into an Airflow DAG to reject runs where data fails validation. Include example rules for column types, min/max values, null thresholds, and unique constraints and sketch the DAG tasks that enforce and record results.

EasyBehavioral

0 practiced

Tell me about a time you discovered a data issue that threatened a model's performance or a production decision. Use the STAR framework: describe the Situation, your Task, the Actions you took (diagnosis steps, immediate mitigation), the Result, and what long-term changes you implemented to prevent recurrence.

EasyTechnical

0 practiced

Describe the purpose of a feature store for ML teams and explain how a properly designed feature store helps prevent leakage and enables reproducible training and serving. What metadata and contract elements should a feature store expose to assist automatic validation, lineage, and runtime enforcement?

Unlock Full Question Bank

Get access to hundreds of Data Validation, Leakage Prevention & Statistical Rigor interview questions and detailed answers.

Join thousands of developers preparing for their dream job.