A data scientist is building a model to predict customer credit scores based on information...

→ XGBoost (Extreme Gradient Boosting) is a high-performance, scalable ensemble algorithm that builds decision trees in sequence and adjusts to errors iteratively. It also supports incremental training, making it adaptive to changing data patterns — ideal for dynamically updated credit information.

Why the other options are incorrect:

A: Decision trees are static once trained and don’t adapt unless retrained.

B: Random forest is an ensemble of trees but lacks the adaptive boosting component.

C: LDA is a linear classification technique — not suited for adapting to changing data distributions.

Official References:

CompTIA DataX (DY0-001) Official Study Guide – Section 4.3:“XGBoost is highly efficient and supports iterative learning, making it well-suited for data environments that evolve over time.”

Applied Machine Learning Guide, Chapter 8:“XGBoost adapts to changes by refining errors across iterations, providing robustness in dynamic systems.”

—