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What is BigQuery ML and when should you use it over Vertex AI custom training?
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BigQuery ML lets you create, train, and evaluate ML models using SQL directly inside BigQuery — no data export, no custom code. Use it when: your data already lives in BigQuery, your team has SQL expertise but limited ML engineering skills, and supported algorithms (linear/logistic regression, k-means, XGBoost, DNNs, time series) fit your use case. Choose Vertex AI custom training when you need custom architectures, full framework control, or algorithms not supported in BQML.
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GCP Professional ML Engineer is a Google Cloud certification. These facts come from the current exam guide and are the numbers to plan around.
| Domain | Weight | What to focus on |
|---|---|---|
| Architecting Low-Code ML Solutions | 17% | BigQuery ML, Vertex AI AutoML, Pre-built APIs |
| Collaborating Within and Across Teams | 17% | TFDV, Vertex Explainable AI, Model cards |
| Scaling Prototypes Into ML Models | 17% | Custom Training, Distributed Training, Hyperparameter Tuning |
| Serving and Scaling Models | 17% | Vertex AI Endpoints, Online Prediction, Batch Prediction |
| Automating and Orchestrating ML Pipelines | 16% | Vertex AI Pipelines, KFP SDK, Feature Store |
| Monitoring ML Solutions | 16% | Model Monitoring, Drift Detection, Retraining Triggers |
Yes. You can start Professional ML Engineer practice and flashcards free without creating an account. A free account keeps your progress, and the full bank (300+ questions) unlocks with a subscription.
They cover all 6 domains of the Professional ML Engineer exam: Architecting Low-Code ML Solutions (17%), Collaborating Within and Across Teams (17%), Scaling Prototypes Into ML Models (17%), Serving and Scaling Models (17%), Automating and Orchestrating ML Pipelines (16%), Monitoring ML Solutions (16%). Each section is weighted in the same proportion as the real exam, so nothing high-value is buried.
The Professional ML Engineer exam has approximately 60 questions in 120 minutes. Use the practice questions to confirm you are consistently above that line before booking the real exam.
Study in short, spaced sessions: a card you have not seen for two days is worth more than a card you reread immediately. Filter by Architecting Low-Code ML Solutions first, then mix domains so you practise identifying the topic before recalling the definition.
They are the recall half of preparation. Pair the cards with practice questions and at least one timed practice exam — candidates who only review cards usually recognise terms but miss scenario questions.