Data Preprocessing — Google Cloud ML Engineer Practice Questions
Data preprocessing encompasses all transformations applied to raw data before it is fed into a model, including handling missing values, encoding categorical variables, scaling numeric features, and splitting datasets. The exam tests candidates on implementing preprocessing with tools such as Dataflow for large-scale distributed transformations, the Vertex AI Feature Store for managing and serving reusable features, and TensorFlow Extended components for embedding preprocessing in the training pipeline. A key concept is avoiding training-serving skew by ensuring the same preprocessing logic runs identically during training and at prediction time.
Free questions on data preprocessing
What is the purpose of normalization in machine learning data preprocessing?
Free question · easy · full answer + explanation
What is the primary purpose of feature engineering in machine learning?
Free question · medium · full answer + explanation
More data preprocessing questions in the full bank
- What is the primary risk of using simple random oversampling for imbalanced data? Unlock answer & explanation →
- Your training dataset has missing values in 15% of records for a critical feature. What is the best imputation strategy for time-series data? Unlock answer & explanation →
- What is the primary difference between normalization and standardization? Unlock answer & explanation →