PMLE Architecting Low-Code ML Solutions Practice Question
A healthcare organization wants to build a model to predict patient readmission risk using structured electronic health record (EHR) data. They need to train a model using SQL in BigQuery, but they also want to leverage AutoML's ability to automatically search for the best architecture. Which approach should they take?
⚠ Common exam trap
It's easy for candidates to confuse AutoML Tables (a separate Vertex AI service) with BigQuery ML's built-in AUTO model type, assuming they must export data to use AutoML, when in fact BigQuery ML provides AutoML capabilities directly within SQL.
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 BigQuery ML with the AUTOML_CLASSIFIER model type
BigQuery ML's AUTOML_CLASSIFIER model type automatically performs architecture search and hyperparameter tuning, making it ideal for users who want to leverage AutoML capabilities directly within SQL on structured EHR data. This approach avoids manual model selection while staying entirely within BigQuery's SQL interface, which is the stated requirement.
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 a pre-built Vision API model via BigQuery ML remote model
Why it's wrong here
A Vision API remote model performs image classification, so it cannot consume structured EHR tabular data or predict readmission. It is tempting because BigQuery ML remote models let SQL users call pre-built models, which is correct when the input is images or text rather than numeric and categorical clinical features.
- ✓
Use BigQuery ML with the AUTOML_CLASSIFIER model type
Why this is correct
BigQuery ML's AUTOML_CLASSIFIER model type trains directly on BigQuery data using SQL while running AutoML architecture search and hyperparameter tuning behind the scenes. This satisfies both the SQL-in-BigQuery requirement and the demand for automated model selection on structured EHR data.
- ✗
Use AutoML Tables with Vertex AI and export predictions
Why it's wrong here
AutoML Tables trains and predicts through the Vertex AI API, not SQL inside BigQuery, so it fails the requirement to train using SQL. It is tempting because it performs automatic architecture and hyperparameter search on structured tabular data, which suits teams working in Vertex AI rather than BigQuery.
- ✗
Use BigQuery ML with a DNN_CLASSIFIER and manual hyperparameter tuning
Why it's wrong here
DNN_CLASSIFIER with manual tuning requires the user to select and search hyperparameters by hand, so it does not deliver AutoML's automatic architecture search. It is tempting because it trains tabular classifiers entirely in SQL, which fits when you want full control over architecture and tuning rather than automated search.
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