PMLE Architecting Low-Code ML Solutions Practice Question
A company uses BigQuery ML to train a boosted tree classifier on a large dataset. After training, they want to understand which features most influence predictions. Which BigQuery ML function should they use?
⚠ Common exam trap
PMLE often tests the confusion between global feature importance (ML.FEATURE_IMPORTANCE) and local per-prediction explanations (ML.EXPLAIN_PREDICT) — candidates pick EXPLAIN_PREDICT because it sounds more 'explanatory,' but it returns Shapley values per row, not a global ranking.
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
✓
ML.FEATURE_IMPORTANCE
ML.FEATURE_IMPORTANCE is the BigQuery ML function specifically designed to return the relative importance of each input feature for a trained model, including boosted tree classifiers (Boosted Tree, XGBoost, Random Forest). It computes importance scores using the model's internal split-gain or weight-based metrics, giving a ranked list of features that most influence predictions. This is the direct, purpose-built answer for global feature attribution on a trained model.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
ML.EXPLAIN_PREDICT
Why it's wrong here
ML.EXPLAIN_PREDICT returns per-row explanations and feature attributions for individual predictions, not a global ranking of which features most influence the model overall. Global feature importance would come from ML.GLOBAL_EXPLAIN, so this function answers a different question.
- ✓
ML.FEATURE_IMPORTANCE
Why this is correct
ML.FEATURE_IMPORTANCE returns a per-feature score showing how strongly each input influenced the trained boosted tree model's predictions. It is the BigQuery ML function designed for interpreting model behaviour, satisfying the requirement to identify the most influential features.
- ✗
ML.EVALUATE
Why it's wrong here
ML.EVALUATE returns aggregate performance metrics such as accuracy, precision, recall, and AUC by comparing predicted labels against actual labels, so it exposes nothing about individual feature contributions to predictions. It is tempting because it is the standard post-training diagnostic, and it would be the right choice when the goal is to assess model quality or compare candidate models rather than explain feature influence.
- ✗
ML.PREDICT
Why it's wrong here
ML.PREDICT applies a trained model to new data to generate predicted labels or values; it returns no feature attribution. It is tempting because it is the standard function for scoring after training, and would be correct when the company needs batch predictions on unlabeled rows rather than insight into which inputs drove the classifier's decisions.
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Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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