Data Preprocessing and Handling for AI Questions

Covers the end to end preparation of raw data for analysis and modeling in machine learning and artificial intelligence. Topics include data collection and ingestion, data quality assessment, detecting and handling missing values with deletion or various imputation strategies, identifying and treating outliers, removing duplicates, and standardizing formats such as dates and categorical labels. Includes data type conversions, categorical variable encoding, feature scaling and normalization, standardization to zero mean and unit variance, and guidance on when each is appropriate given model choice. Covers feature engineering and selection, addressing class imbalance with sampling and weighting methods, and domain specific preprocessing such as data augmentation for computer vision and text preprocessing for natural language processing. Emphasizes correct order of operations, reproducible pipelines, splitting data into training validation and test sets, cross validation practices, and documenting preprocessing decisions and their impact on model performance. Also explains which models are sensitive to feature scale, common pitfalls, and evaluation strategies to ensure preprocessing does not leak information.

HardSystem Design

0 practiced

Design an end-to-end preprocessing service for real-time model inference that must handle 1,000 requests/sec with end-to-end preprocessing latency <50ms. Describe components (feature store/cache, online transforms, categorical mappings, schema validation, monitoring), choices between serverless vs managed servers, how to keep offline and online consistency, and fallback strategies if the feature store is unavailable.

MediumTechnical

0 practiced

Explain why standard k-fold cross-validation is inappropriate for many time-series tasks. Describe at least two time-aware strategies (rolling-window / walk-forward validation and expanding window) and outline how you'd implement them in code or SQL for a forecasting pipeline.

EasyTechnical

0 practiced

Explain the difference between min-max normalization and z-score standardization. For each method describe how it handles outliers, when you would apply it (e.g., for neural networks vs. when preserving distribution matters), and how you would invert the transform for interpretability in production.

HardSystem Design

0 practiced

Design an orchestration pattern for incremental preprocessing jobs that feed features to models daily. Describe scheduling, incremental loaders, idempotency, checkpointing, failure and retry strategies, data validation (schema and value checks), monitoring/alerting, and SLA enforcement. Recommend concrete technologies (Airflow, Prefect, dbt, Spark) and justify your choices.

EasyTechnical

0 practiced

In Python using pandas, write concise code (or outline code) to: 1) identify numeric columns with missing values; 2) compute medians on the training set and impute numeric columns in validation/test using those medians; 3) for a time-series categorical column, perform forward-fill after sorting by timestamp. Show how you'd wrap this into a reproducible function.

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