Large Dataset Management and Technical Analysis Questions
Develop skills in working efficiently with large datasets: data cleaning and validation, efficient aggregation and manipulation, handling missing data, identifying and managing outliers. Master advanced Excel features or learn SQL for database queries. Practice data quality assessment. Learn efficient workflows that scale with dataset size. Understand data security and privacy considerations.
EasyTechnical
37 practiced
In Python/pandas, show (or describe) how you would convert object/string columns to pd.Categorical and downcast numeric types to reduce memory on a 10M-row dataset. Explain trade-offs (e.g., category overhead, effect on joins) and how you'd validate memory savings.
EasyTechnical
45 practiced
You have a 'transactions' table with schema: transaction_id PK, user_id INT, transaction_time TIMESTAMP, amount DECIMAL, external_id TEXT. Duplicate rows can exist for the same external_id. Write a safe PostgreSQL statement that removes duplicates while keeping the latest transaction_time per external_id. Describe how you would perform this operation on a 200M-row production table with minimal downtime.
MediumTechnical
46 practiced
You have a 200M-row dataset with numeric and categorical features containing missing values. Compare imputation strategies (mean/median/mode, indicator variables, KNN, MICE, model-based imputation) focusing on scalability, bias introduced, and what automated validations you'd run before deploying imputed data for model training.
HardTechnical
39 practiced
Design an offline validation suite that scans training datasets at scale to detect fairness issues (e.g., disparate impact and equalized odds). Specify metrics, sampling strategies to handle small protected groups, thresholds, alerting, and automated remediation actions such as reweighting or constrained optimization. How would you validate the effectiveness of remediations?
MediumTechnical
45 practiced
Explain trade-offs between normalized relational schemas and denormalized wide-feature tables for ML use: query latency, storage duplication, update complexity, referential integrity, and analytics vs ML access patterns. Provide recommendations for offline training and online serving.
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