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PMLE Practice Question: A retail company wants to build a customer churn…
A retail company wants to build a customer churn prediction model using BigQuery ML. The data is stored in BigQuery tables and includes customer demographics, purchase history, and support interactions. The data scientist wants to experiment with different model types quickly without moving data to another environment. Which approach should they use?
⚠ Common exam trap
Google Cloud often tests the candidate's ability to recognize that BigQuery ML is purpose-built for low-code, in-database ML experimentation, and the trap here is assuming that more complex or external tools (like Vertex AI or Cloud Composer) are necessary when the simpler, integrated solution suffices.
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 to create and evaluate models directly in BigQuery.
BigQuery ML (BQML) allows data scientists to create, train, and evaluate machine learning models directly in BigQuery using SQL, without moving data to another environment. This approach supports rapid experimentation with various model types (e.g., logistic regression, boosted trees, deep neural networks) and is ideal for the stated requirement of quick iteration while keeping data in place.
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 Cloud Composer to orchestrate a custom training pipeline on Vertex AI.
Why it's wrong here
Cloud Composer orchestrates a custom Vertex AI training pipeline, requiring data movement and pipeline engineering rather than quick in-place experimentation. It is the right choice for scheduled, productionised multi-step workflows, but not for rapidly comparing model types directly against BigQuery tables.
- ✗
Use AI Platform Notebooks with pandas and scikit-learn.
Why it's wrong here
Notebooks with pandas and scikit-learn pull data out of BigQuery into a separate environment, breaking the no-movement requirement. This approach suits bespoke feature engineering and algorithm control, but BigQuery ML trains multiple model types in place with SQL, which the scenario demands.
- ✓
Use BigQuery ML to create and evaluate models directly in BigQuery.
Why this is correct
BigQuery ML trains and evaluates models using SQL directly against the existing BigQuery tables, so no data movement or separate serving environment is needed. This satisfies the requirement to experiment with multiple model types quickly in place.
- ✗
Export the data to Cloud Storage and use Vertex AI AutoML Tables.
Why it's wrong here
Exporting to Cloud Storage and AutoML Tables moves data out of BigQuery, contradicting the requirement to experiment without relocating it. AutoML Tables suits teams wanting managed tabular training on external datasets, but here it adds export and latency instead of in-place BigQuery ML training.
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