A data scientist is designing a real-time machine-learning model that classifies a user based on...

→ In real-time systems, low latency (short run time) is critical. While the Artificial Neural Network provides the highest accuracy, its 12-minute runtime makes it unsuitable for real-time inference. Random forest is the fastest but offers the lowest accuracy.

XGBoost provides an excellent balance between runtime (5 minutes) and accuracy (90%). It’s well-optimized for performance and scalability, and thus is a strong candidate for real-time classification when balancing both efficiency and predictive quality.

Why the other options are less ideal:

B: Random forest is faster but significantly less accurate.

C: Decision trees have longer run time than XGBoost with only a 2% accuracy improvement.

D: Artificial neural network has the highest accuracy but is too slow for real-time applications.

Official References:

CompTIA DataX (DY0-001) Official Study Guide – Section 4.3:“In real-time applications, model selection involves a trade-off between accuracy and inference speed. XGBoost offers competitive accuracy with efficient runtime.”

Machine Learning Systems Design Guide, Chapter 7:“XGBoost is well-suited for real-time systems due to its balance of model complexity and fast prediction times.”

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