easyMultiple ChoiceObjective-mapped
PMLE Practice Question: A data scientist wants to quickly train a binary…
A data scientist wants to quickly train a binary classification model on a tabular dataset stored in BigQuery without writing any code. They have limited ML experience. Which Google Cloud service should they use?
⚠ Common exam trap
Google Cloud often tests the distinction between 'low-code' (BigQuery ML) and 'no-code' (AutoML) services, but the trap here is that AutoML Tables requires more setup and data movement, while BigQuery ML is the fastest no-code option for users already working in BigQuery.
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 with CREATE MODEL statement using SQL.
BigQuery ML allows a data scientist to train a binary classification model directly in BigQuery using a `CREATE MODEL` SQL statement, without writing any code or moving data. This is the fastest low-code approach for users with limited ML experience, as it leverages familiar SQL syntax and runs entirely within BigQuery's serverless infrastructure.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Vertex AI Workbench with a built-in scikit-learn notebook.
Why it's wrong here
Requires Python coding and environment setup.
- ✗
Dataflow with a TensorFlow pipeline.
Why it's wrong here
Dataflow is for data processing, not training.
- ✓
BigQuery ML with CREATE MODEL statement using SQL.
Why this is correct
BigQuery ML enables model creation with SQL, no coding required.
- ✗
AutoML Tables with a direct BigQuery connection.
Why it's wrong here
AutoML Tables is not SQL-based; it requires a UI or API.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
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