PMLE Architecting Low-Code ML Solutions Practice Question
A data analyst wants to train a binary classification model in BigQuery ML on a dataset of 10 million rows with 50 features. They need to evaluate the model's performance on a held-out test set. Which sequence of SQL statements should they run?
⚠ Common exam trap
The trap is confusing ML.EVALUATE with ML.PREDICT or thinking that ML.TRAIN exists. Candidates might also think they need to use ML.FEATURE_IMPORTANCE for evaluation.
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
✓
CREATE MODEL then ML.EVALUATE
To train and evaluate a model in BigQuery ML, you use CREATE MODEL to train the model, and then ML.EVALUATE to assess its performance on a held-out dataset. CREATE MODEL automatically splits the data into training and evaluation sets if specified, or you can use a separate table for evaluation. ML.EVALUATE returns metrics like accuracy, precision, recall, etc. This sequence is standard for model development in BigQuery ML.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
CREATE MODEL then ML.FEATURE_IMPORTANCE
Why it's wrong here
ML.FEATURE_IMPORTANCE returns per-feature contribution scores for an already-trained model, not held-out performance metrics. It is tempting because it aids model interpretation after training, and would be correct when explaining which of the 50 features drove predictions rather than evaluating accuracy on a test set.
- ✗
ML.TRAIN then ML.EVALUATE
Why it's wrong here
BigQuery ML has no ML.TRAIN function; models are created with CREATE MODEL, which performs training. The name is tempting because it mirrors the train-then-evaluate workflow, and such a sequence would be correct in frameworks exposing separate training and evaluation calls, unlike BigQuery ML's SQL syntax.
- ✗
CREATE MODEL then ML.PREDICT
Why it's wrong here
ML.PREDICT generates predicted labels or probabilities on input rows; it computes no evaluation metrics such as precision, recall or AUC against a held-out test set. It is tempting because scoring new data is a genuine BigQuery ML task, and would be correct when applying the trained model to unlabelled records.
- ✓
CREATE MODEL then ML.EVALUATE
Why this is correct
BigQuery ML trains the model with CREATE MODEL, which handles the 10-million-row, 50-feature dataset natively in SQL. ML.EVALUATE then scores that trained model against the held-out test set, returning precision, recall, AUC and related metrics, satisfying the evaluation requirement without exporting data.
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Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
This PMLE practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PMLE exam.