Standards and Governance Questions

Evaluate the candidate ability to define, establish, and communicate standards and best practices that raise quality and consistency across teams. This includes creating standards for data quality, engineering practices, code review, security hygiene, testing, and documentation, as well as processes for adoption, enforcement, and continuous improvement. Candidates should discuss stakeholder engagement strategies, change management to shift culture without formal authority, mechanisms for measuring compliance and impact, and examples of standards they introduced or improved and the organizational outcomes.

MediumTechnical

0 practiced

Provide Python pseudocode using pandera (or a similar library) that defines a schema for the transactions table, validates a pandas DataFrame, and raises structured errors suitable for CI to fail. Then show a simple pytest test that calls this validation and fails the CI build if the schema is violated. Explain how error messages should be surfaced to owners.

HardTechnical

0 practiced

A production model's AUC dropped by 5% after enforcing stricter feature validation rules. You own the incident response. Describe a step-by-step investigation plan to identify whether the drop is due to the validation rules (e.g., dropped features, changed imputations), data distribution shifts, label issues, or model regressions. Propose experiments to measure trade-offs, rollback criteria, and how to communicate remediation and lessons to stakeholders.

MediumTechnical

0 practiced

You piloted unit tests for feature engineering in one team and want to quantify the impact before wider rollout. Describe an experimental design to evaluate the change: define treatment and control, metrics to collect (e.g., bugs found post-deployment, rollback frequency, cycle time), required duration/sample size, possible confounders, and decision criteria to proceed to rollout.

EasyTechnical

0 practiced

Propose three measurable KPIs to assess compliance with a new code-review policy for data science code and notebooks. For each KPI explain how you would compute it from Git metadata and what a healthy threshold might look like. Include at least one KPI focused on quality, one on speed, and one on coverage.

EasyTechnical

0 practiced

Propose a concise and scalable naming convention for datasets, feature tables, and model artifacts in a multi-team organization. Your convention should include components such as team, project, environment (dev/staging/prod), logical name, and version. Provide three concrete examples (one dataset, one feature table, one model artifact) and describe rules for backwards compatibility and deprecation.

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