Technical practice
How do you approach data profiling and validate data accuracy, completeness, and consistency using Python and SQL?
What to coverCandidates should discuss writing automated scripts in Python and SQL to check for missing values, duplicate records, distribution anomalies, and referential integrity. Emphasize how profiling lays the foundation for reliable data science solutions.
What strategies do you use for validating data ingestion and feature engineering pipelines?
What to coverExplain how to test ETL and feature transformation steps to ensure that data transformations do not introduce bias or data corruption. Discuss verifying schema consistency and monitoring feature definitions before model consumption.
How do you design and execute test plans for machine learning solutions and analytical workflows?
What to coverDescribe the structured approach of translating business requirements into comprehensive test cases that cover functional behavior, data constraints, and performance parameters. Mention how you handle edge cases in analytical workloads.
How do you apply statistical testing to verify analytical outputs and ensure dataset reliability?
What to coverDiscuss using hypothesis testing, p-values, confidence intervals, and distribution comparisons to confirm whether analytical outputs are statistically sound. Explain how statistical tests catch subtle data quality defects.
What metrics and methodologies do you use when evaluating machine learning model results?
What to coverCover standard evaluation metrics appropriate for different model types (e.g., precision, recall, F1, RMSE) and how you validate model performance against baseline expectations. Mention the importance of evaluating model outputs under different operational conditions.
How do you monitor and detect data drift in production machine learning models?
What to coverExplain concepts related to covariate shift and concept drift, and discuss how you set up automated monitoring to alert teams when incoming production data deviates significantly from training distributions.
How have you utilized cloud platforms like Amazon SageMaker or Bedrock in testing or deploying AI solutions?
What to coverDiscuss experience with cloud-based machine learning environments, evaluating managed endpoints, and testing model deployment pipelines. Address how cloud services facilitate scalable evaluation frameworks.
How do you approach test automation for data science applications compared to traditional software?
What to coverHighlight the challenges of non-deterministic model behavior, probabilistic outputs, and dynamic data dependencies. Explain how you build robust regression testing frameworks for data pipelines.
Behavioural practice
Tell me about a time when you uncovered a critical data quality issue late in a project lifecycle.
What to coverDescribe the situation, the specific automated or manual profiling technique you used to identify the anomaly (action), and the resulting impact on project reliability and stakeholder alignment (result).
Describe a situation where you had to collaborate closely with data scientists to resolve a model performance discrepancy.
What to coverExplain the context of the disagreement or discrepancy, how you communicated your test findings and evaluation metrics (action), and how the collaboration led to a more robust final solution (result).
Give an example of how you prioritized multiple testing tasks under tight project deadlines.
What to coverOutline the competing priorities, your approach to risk-based test planning and automated script execution (action), and how you ensured core data pipeline integrity without missing deadlines (result).
Tell me about a time when you had to advocate for rigorous data validation standards to non-technical stakeholders.
What to coverExplain the context where quality standards were being rushed, how you articulated the business risks of poor data quality (action), and the positive outcome of implementing your recommended validation checks (result).