Quick Answer
The correct answer is that the BigQuery ML PREDICT command makes predictions using a trained model. This command, written as `ML.PREDICT` in a SQL query, takes a specified model and applies it to new input data to generate inference results, such as predicted labels or values, without modifying the model itself. On the Google Professional Machine Learning Engineer exam, this tests your understanding of the ML workflow stages—specifically that prediction is a distinct inference step separate from training, evaluation, or export. A common trap is confusing `ML.PREDICT` with `ML.TRAIN` or `ML.EVALUATE`, so remember that PREDICT is for outputting forecasts on fresh data, not for building or assessing the model. Memory tip: “PREDICT = Produce Results from Existing Data In a Trained model.”
This PMLE practice question tests your understanding of architecting low-code ml solutions. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
bq query --use_legacy_sql=false 'SELECT * FROM ML.PREDICT(MODEL mydataset.mymodel, (SELECT * FROM mydataset.newdata))'
Refer to the exhibit. What does this command do?
bq query --use_legacy_sql=false 'SELECT * FROM ML.PREDICT(MODEL mydataset.mymodel, (SELECT * FROM mydataset.newdata))'
Trains a new BigQuery ML model
Why wrong: CREATE MODEL is used for training.
Exports the model to Cloud Storage
Why wrong: EXPORT MODEL is used for exporting.
Evaluates the model's performance
Why wrong: ML.EVALUATE is used for evaluation.
Makes predictions using the model
ML.PREDICT generates predictions.
Answer choices
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 shown in the exhibit is a BigQuery ML prediction query (e.g., `SELECT * FROM ML.PREDICT(MODEL mydataset.mymodel, ...)`). This command uses a trained model to generate predictions on new input data, making option D correct. It does not train, export, or evaluate the model.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
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
CREATE MODEL is used for training.
Exports the model to Cloud Storage
Why it's wrong here
EXPORT MODEL is used for exporting.
Evaluates the model's performance
Why it's wrong here
ML.EVALUATE is used for evaluation.
Makes predictions using the model
Why this is correct
ML.PREDICT generates predictions.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
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.
Detailed technical explanation
Under the hood, `ML.PREDICT` invokes the model's serving function, applying the trained weights to the input features and returning the predicted label or value. In BigQuery ML, this function can also output additional columns like `predicted_label` and `predicted_<label>_probs` for classification models. A real-world scenario is using a churn prediction model to score a batch of customer records stored in a BigQuery table, enabling real-time or batch inference without moving data.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
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FAQ
Architecting low-code ML solutions — This question tests Architecting low-code ML solutions — Read the scenario before looking for a memorised answer..
The correct answer is: Makes predictions using the model — The command shown in the exhibit is a BigQuery ML prediction query (e.g., `SELECT * FROM ML.PREDICT(MODEL mydataset.mymodel, ...)`). This command uses a trained model to generate predictions on new input data, making option D correct. It does not train, export, or evaluate the model.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
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