AI0-001 Machine Learning and Deep Learning Practice Question
A retail company uses a gradient boosting model to predict customer lifetime value (CLV). The model currently uses 50 features including purchase history, demographics, and web behavior. The model's RMSE on the test set is 120. The data science team wants to improve the model's accuracy without increasing training time significantly. They have access to additional data: customer support interaction logs (text), social media sentiment (text), and third-party credit scores (numeric). They also have the ability to perform feature engineering, hyperparameter tuning, and ensemble methods. Which approach is most likely to yield the best improvement in predictive performance with minimal increase in training time?
⚠ Common exam trap
CompTIA often tests the misconception that adding more data (especially text) or complex ensemble methods always improves model accuracy, while the correct approach is to engineer features that capture domain-specific patterns with minimal computational overhead.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Engineer new features such as average purchase value and recency
Engineering domain-relevant features like average purchase value and recency directly captures the underlying behavioral patterns that drive customer lifetime value, often providing a higher signal-to-noise ratio than adding raw text or third-party data. This approach leverages existing data without significantly increasing the feature dimensionality or training time, unlike adding TF-IDF vectors which would dramatically expand the feature space and slow training.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Add the customer support text as a feature using TF-IDF vectors
Why it's wrong here
Adding text features requires significant preprocessing and may increase training time.
- ✗
Use an ensemble of gradient boosting and random forest models
Why it's wrong here
Ensemble methods increase training time and may not be justified.
- ✗
Perform hyperparameter tuning using grid search
Why it's wrong here
Hyperparameter tuning can help but may not provide the largest improvement.
- ✓
Engineer new features such as average purchase value and recency
Why this is correct
Feature engineering can capture patterns without adding new data sources or significant time.
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Written by Johnson Ajibi, MSc IT Security
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