AI and Machine Learning Background Questions
A synopsis of applied artificial intelligence and machine learning experience including models, frameworks, and pipelines used, datasets and scale, production deployment experience, evaluation metrics, and measurable business outcomes. Candidates should describe specific projects, roles played, research versus production distinctions, and technical choices and trade offs.
EasyTechnical
0 practiced
Define training-serving skew and dataset shift in the context of BI reports that show model predictions. Give two concrete examples of each from product analytics data and list five quick dashboard checks a BI Analyst can add to surface skew early.
MediumTechnical
0 practiced
Write a SQL snippet suitable for Looker/LookML or Power BI to join precomputed SHAP values (schema: shap_values(user_id bigint, feature text, shap_value float, model_version text)) to a user segment table and compute the top 3 features by average absolute SHAP per user segment for a specified model_version.
HardTechnical
0 practiced
You must reduce false positives from a fraud detection model that generates customer friction. Propose a multi-pronged solution combining threshold tuning, a calibrated second-stage model, human-in-the-loop review, and BI reporting changes. Quantify expected trade-offs and describe how you would monitor impact post-change.
HardTechnical
0 practiced
A regulatory audit requests explanations for loan approvals made by a model over the last six months. As the BI Analyst, design the report and evidence package you would assemble: include model version history, input features used, SHAP explanations or feature importances, data lineage, retraining dates, human overrides, and a retrieval strategy for these artifacts.
EasyTechnical
0 practiced
What is model explainability and why is it important for BI dashboards? Compare LIME and SHAP at a high level, describe a scenario where you would include feature contribution explanations in an executive dashboard, and list two limitations you must communicate to stakeholders.
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