easyMultiple Select
Generative AI Leader Practice Question: A data scientist wants to run ML models directly…
A data scientist wants to run ML models directly on their BigQuery data without moving data out. Which THREE statements about BigQuery ML are correct? (Choose 3)
⚠ Common exam trap
A common misconception is that BigQuery ML supports all model types, including CNNs and RNNs. In fact, BigQuery ML supports a specific set of model types such as linear and logistic regression, matrix factorization, boosted trees, and deep neural networks (DNN), but it does not natively support CNNs or RNNs. Also, BigQuery ML does not require exporting data to Cloud Storage before training.
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
✓
BigQuery ML supports binary logistic regression models
BigQuery ML lets data scientists build and evaluate models directly on data in BigQuery using SQL, without exporting data. It supports binary logistic regression via CREATE MODEL with OPTIONS(model_type='LOGISTIC_REG') for classification, linear regression via OPTIONS(model_type='LINEAR_REG') for forecasting and regression, and recommendation systems via matrix factorization with OPTIONS(model_type='MATRIX_FACTORIZATION'). All three can be trained and evaluated in place using standard SQL.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
BigQuery ML supports binary logistic regression models
Why this is correct
Binary logistic regression is supported for classification tasks using SQL.
- ✗
BigQuery ML supports deep neural network models like CNNs and RNNs
Why it's wrong here
BigQuery ML does not directly support complex deep learning architectures; those are built in Vertex AI.
- ✗
BigQuery ML requires data to be exported to Cloud Storage before training
Why it's wrong here
BigQuery ML trains directly on data in BigQuery tables without requiring export.
- ✓
BigQuery ML supports creating and evaluating linear regression models using SQL
Why this is correct
BigQuery ML can create linear regression models with CREATE MODEL statements and evaluate with ML.EVALUATE.
- ✓
BigQuery ML can be used for recommendation systems using matrix factorization
Why this is correct
BigQuery ML provides matrix factorization for building recommendation models.
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