PMLE Architecting Low-Code ML Solutions Practice Question
A data analyst wants to use BigQuery ML to train a linear regression model (LINEAR_REG) to predict house prices. They have a table with features like square footage, number of bedrooms, and location. Which TWO statements about the training process are correct?
⚠ Common exam trap
A common misconception is that BigQuery ML requires an explicit training command (like `ML.TRAIN`) or that models are stored in Cloud Storage by default, when in fact training is fully encapsulated in `CREATE MODEL` and models reside in BigQuery's internal storage.
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
✓
The model is automatically evaluated on a held-out test set if data splitting is enabled
When data splitting is enabled in BigQuery ML, the `CREATE MODEL` statement automatically reserves a portion of the input data as a held-out test set. After training completes, BigQuery ML evaluates the model on this test set and reports metrics like mean absolute error and R², without requiring any manual split or separate evaluation step.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The analyst must call ML.TRAIN after CREATE MODEL to start training
Why it's wrong here
CREATE MODEL itself triggers and completes training synchronously, so no separate ML.TRAIN call exists in BigQuery ML. The tempting confusion arises from frameworks where fitting is a distinct step after defining architecture; here the CREATE MODEL statement with the OPTIONS clause performs both definition and training in one query.
- ✗
The trained model is stored in Cloud Storage
Why it's wrong here
BigQuery ML stores trained models as objects inside a BigQuery dataset, addressable via ML.PREDICT and the model resource name, not in Cloud Storage. Cloud Storage holds exported model artefacts or training data; it would be the destination only if the analyst explicitly ran ML.EXPORT_MODEL to serve the model elsewhere.
- ✗
The model must be exported to Vertex AI for prediction
Why it's wrong here
BigQuery ML serves predictions directly from the trained model using ML.PREDICT, so exporting to Vertex AI is not required. It is tempting because Vertex AI hosts custom models, and export is correct when deploying outside BigQuery or serving at scale.
- ✓
The model is automatically evaluated on a held-out test set if data splitting is enabled
Why this is correct
Enabling data splitting in CREATE MODEL reserves a portion of the input rows as a held-out test set, and BigQuery ML automatically computes evaluation metrics on it after training, satisfying the requirement to assess model quality without a separate manual step.
- ✓
Training is performed using the CREATE MODEL statement
Why this is correct
BigQuery ML trains models through SQL DDL, so CREATE MODEL with the LINEAR_REG model type is the statement that fits the house-price regression scenario, specifying the input features and label column directly in the query.
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