PMLE Architecting Low-Code ML Solutions Practice Question
A data scientist wants to evaluate the performance of a BigQuery ML classification model on a test dataset. Which function should they use?
⚠ Common exam trap
Google often tests the distinction between prediction (ML.PREDICT) and evaluation (ML.EVALUATE), trapping candidates who confuse generating outputs with measuring performance, especially when the question mentions 'evaluate performance' but the candidate fixates on 'predict' as the primary ML function.
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.EVALUATE
ML.EVALUATE is the correct function because it computes classification metrics (e.g., precision, recall, accuracy, F1 score, ROC AUC) directly on a trained BigQuery ML model using a provided test dataset or evaluation input. This is the dedicated function for assessing model performance after training, aligning with the task of evaluating a classification model on held-out test data.
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.PREDICT
Why it's wrong here
ML.PREDICT generates predictions from a model against new data; it returns labels and probabilities but computes no accuracy, precision, recall or confusion matrix. It is tempting because it is the natural step before evaluation, yet the question asks for the scoring function that compares predictions with actual test labels.
- ✗
ML.FEATURE_IMPORTANCE
Why it's wrong here
ML.FEATURE_IMPORTANCE ranks input columns by their contribution to a trained model, not how well it classifies unseen rows. It is tempting because it does inform model evaluation, but it explains the model's internals rather than scoring predictions against labels, which is what assessing test-set performance requires.
- ✓
ML.EVALUATE
Why this is correct
ML.EVALUATE computes standard classification metrics such as precision, recall, accuracy, F1 score, log loss and ROC AUC against a labelled dataset. It is the BigQuery ML function designed for assessing an already-trained model's performance, unlike ML.PREDICT, which only returns predictions.
- ✗
ML.TRAIN
Why it's wrong here
ML.TRAIN creates and trains a model from a dataset; it does not score an existing model against held-out data. It is the correct function when building a new model, whereas evaluation of a trained classification model requires ML.EVALUATE.
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Same concept, more angles
1 more way this is tested on PMLE
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. 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?
medium- A.CREATE MODEL then ML.FEATURE_IMPORTANCE
- B.ML.TRAIN then ML.EVALUATE
- C.CREATE MODEL then ML.PREDICT
- ✓ D.CREATE MODEL then ML.EVALUATE
Why D: 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.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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