data-preprocessing-feature-engineering

AI Essentials - Grok · 19 questions

• A team needs to compress high-dimensional numeric features into a smaller set of axes that still capture most of the variation for visualization and modeling. Which technique is commonly used for that dimensionality reduction?
• A binary classifier is trained on a dataset where one class appears far more often than the other. Which approach is a standard way to mitigate that class imbalance?
• What does feature engineering primarily involve in a machine learning workflow?
• In data preprocessing, what is the main purpose of normalizing continuous features?
• Before training a distance-sensitive classifier, you notice age spans roughly 0–100 while annualincome spans tens of thousands to millions. Which preprocessing action best addresses this scale mismatch?
• A data science team is preparing a high-dimensional customer dataset for a predictive model. Which statement best describes the purpose of feature selection?
• What is the main purpose of one-hot encoding in a machine learning pipeline?
• What does dimensionality reduction mean in machine learning practice?
• In a data-centric AI strategy, which focus is considered the key lever for improving model outcomes?
• During the data collection and cleaning stage of a real-world AI project, what problem do teams most commonly face?
• In a standard machine learning project lifecycle, which phase focuses on gathering raw inputs, fixing quality issues, and shaping them into features ready for training?
• While preparing a large text corpus for topic modeling, a practitioner wants a standard preprocessing step that reduces surface-form variety and thus complexity. Which technique fits that goal?
• While cleaning a text dataset, an analyst notices many low-information function words such as "the," "a," and "is." Which preprocessing action specifically targets those terms?
• During computer-vision preprocessing, what is the primary reason teams normalize pixel intensities before training a neural network?
• An environmental team trains a convolutional neural network on satellite imagery to flag deforestation. Before feeding tiles into the CNN, which preprocessing step is most commonly applied to the pixel intensities?
• You train a CNN to distinguish cat photos from dog photos, but the source images arrive at many different heights and widths. Which preprocessing step is required so every sample matches the network’s fixed input shape?
• After a team finishes cleaning its dataset for an AI product—handling missing values, fixing types, and removing obvious junk—what is the most appropriate next stage in a standard AI engineering pipeline?
• In a typical end-to-end AI project workflow, when should data cleaning primarily be performed?
• During AI problem analysis, what is the primary purpose of exploratory data analysis (EDA)?