easyMultiple Choice
PMLE Practice Question: What does the `ML.PREDICT` command do in BigQuery…
Exhibit
bq query --use_legacy_sql=false 'SELECT * FROM ML.PREDICT(MODEL mydataset.mymodel, (SELECT * FROM mydataset.newdata))'
What does the `ML.PREDICT` command do in BigQuery ML?
⚠ Common exam trap
Google Cloud often tests the distinction between the four key BigQuery ML commands (`CREATE MODEL`, `ML.EVALUATE`, `ML.PREDICT`, `EXPORT MODEL`), and the trap here is confusing the prediction function with the evaluation function, especially when the exhibit shows a query that looks like it might be evaluating performance due to the presence of a model name and input data.
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
✓
Makes predictions using the model
The command is likely a BigQuery ML prediction query (e.g., using `ML.PREDICT`) that uses a trained model to generate predictions on new input data, making option D correct. It does not train, export, or evaluate the 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.
- ✗
Trains a new BigQuery ML model
Why it's wrong here
ML.PREDICT performs inference using an already-trained model; training is handled by CREATE MODEL with the relevant model type. Training is the correct choice when building a new model from labelled data, not when generating predictions from existing data.
- ✗
Exports the model to Cloud Storage
Why it's wrong here
ML.PREDICT runs inference against new data and returns predicted values; exporting a model uses ML.EXPORT_MODEL, which writes the trained artefact to Cloud Storage. Export is chosen when serving the model outside BigQuery, for example in Vertex AI or on-premises.
- ✗
Evaluates the model's performance
Why it's wrong here
ML.PREDICT returns predictions by applying a trained model to input data; it does not score or evaluate model quality. Evaluation is performed by ML.EVALUATE, which computes metrics such as precision, recall or RMSE against labelled data. Confusing prediction output with performance measurement is the trap here.
- ✓
Makes predictions using the model
Why this is correct
ML.PREDICT runs a trained BigQuery ML model against a input table and returns predicted values alongside the source rows. It satisfies the scenario's need to score new data, unlike CREATE MODEL, which trains, or ML.EVALUATE, which only reports metrics.
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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.