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

How would you balance rapid experimentation for research teams with the need for standards and governance in production systems? Propose a bifurcated workflow that allows fast iteration while preventing risky artifacts from reaching production: describe environments, access controls, required artifacts for promotion, and guardrails that enforce minimum checks.

EasyTechnical

0 practiced

Write a Python function (can be pseudocode) that takes an expected_schema (dict of column_name -> expected_type) and a pandas.DataFrame df, and returns a JSON-like report listing: missing_columns, extra_columns, type_mismatches, and null_percentage per column. The function should avoid mutating df and should be suitable to run in CI as a fast check. Show sample output format for a 3-column DataFrame.

MediumSystem Design

0 practiced

Describe how you would implement data access policies and auditing on a cloud data platform (e.g., BigQuery or Snowflake) to enforce least-privilege for data scientists. Cover role-based access, temporary elevated access workflow, column-level masking, automatic access reviews, audit log storage and retention, and automated alerts for anomalous access patterns.

MediumTechnical

0 practiced

Describe a standard for experiment tracking using MLflow (or DVC) that your organization should adopt. List required metadata fields and tags (e.g., run_name, dataset_version, code_commit, hyperparameters, metrics, owner, business_case), how to enforce these fields in your tracking process, and a naming convention for runs and registered models. Provide one concrete example run name and the metadata it would include.

HardTechnical

0 practiced

Devise a change management strategy to shift culture toward mandatory peer review among data scientists who value speed. Include stakeholder mapping, pilot design, incentives and recognition, training materials, phased enforcement (soft → hard), metrics to track cultural change, and approaches to handle resistance, especially from senior engineers who feel the policy slows them down.

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