PDE Preparing and Using Data for Analysis Practice Question
A data scientist wants to import a pre-trained TensorFlow model into BigQuery ML for batch predictions. The model is stored in a Cloud Storage bucket. Which statement is correct?
⚠ Common exam trap
The trap is inventing plausible-sounding model_type strings like 'imported_tensorflow' or fake functions like ML.IMPORT_MODEL — candidates who haven't memorized the exact DDL syntax fall for these.
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
✓
Use CREATE MODEL with model_type='tensorflow' and model_path='gs://bucket/model'.
BigQuery ML supports importing TensorFlow models via CREATE MODEL with model_type='tensorflow' and a model_path pointing to a Cloud Storage location containing the SavedModel. This allows batch prediction using ML.PREDICT directly in BigQuery without moving data to Vertex AI.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use CREATE MODEL with model_type='tensorflow' and model_path='gs://bucket/model'.
Why this is correct
`CREATE MODEL` with `model_type='tensorflow'` and a `gs://` `model_path` is the supported import path for TensorFlow SavedModels into BigQuery ML, satisfying the stem's requirement to load a pre-trained model from Cloud Storage for batch prediction via `ML.PREDICT`.
- ✗
Use CREATE MODEL with model_type='imported_tensorflow' and model_path='gs://bucket/model'.
Why it's wrong here
BigQuery ML imports TensorFlow models with CREATE MODEL using model_type='TENSORFLOW', not 'imported_tensorflow'; the latter is not a valid value, so the statement fails. It is tempting because the syntax mirrors other imported-model types, and would be correct if the option specified the supported TENSORFLOW model type with a valid Cloud Storage path.
- ✗
First upload the model to Vertex AI Model Registry, then reference it in BigQuery ML.
Why it's wrong here
BigQuery ML imports TensorFlow models directly from Cloud Storage via CREATE MODEL; Vertex AI Model Registry is not part of the batch-prediction import path. It is tempting because Vertex AI is Google's model-hosting service, but registering there adds no capability BigQuery ML requires for this task.
- ✗
Use the ML.IMPORT_MODEL function to load the model into BigQuery.
Why it's wrong here
ML.IMPORT_MODEL does not exist in BigQuery ML; the documented import path is CREATE MODEL with the imported_tensorflow model type. It is tempting because a dedicated import function seems intuitive, but BigQuery ML exposes model loading only through the CREATE MODEL DDL statement.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
This PDE 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 PDE exam.