easyMultiple Choice
PMLE Practice Question: A data analyst wants to create a classification…
A data analyst wants to create a classification model directly in BigQuery using SQL. Which feature should they use?
⚠ Common exam trap
Google Cloud often tests the distinction between services that run inside BigQuery (BQML) versus external ML platforms (Vertex AI), trapping candidates who think any ML service qualifies without checking if it operates directly via SQL 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
BigQuery ML (BQML) enables users to create and execute machine learning models directly in BigQuery using standard SQL syntax, without needing to export data or manage separate ML infrastructure. For a data analyst who wants to build a classification model entirely within BigQuery, BQML provides the CREATE MODEL statement with classification algorithms like logistic regression or XGBoost, making it the correct and most direct feature.
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
Why this is correct
BigQuery ML lets analysts build and train models using SQL directly inside BigQuery, satisfying the requirement to create a classification model without leaving SQL or exporting data. It supports models such as logistic regression and boosted trees via CREATE MODEL statements, so no separate machine-learning tooling or Python is needed.
- ✗
Vertex AI
Why it's wrong here
Vertex AI trains models through notebooks, Python SDKs or AutoML, not SQL statements inside BigQuery. It is tempting because it is Google Cloud's flagship machine learning platform, and it would be correct when the analyst needs custom training code, pipelines or model monitoring beyond SQL.
- ✗
Dataflow
Why it's wrong here
Dataflow is a managed Apache Beam runner for building batch and streaming pipelines, not a SQL interface for training models. It is tempting because it processes data inside Google Cloud alongside BigQuery, and it would be the right choice when the requirement is a custom ETL or streaming pipeline rather than model training.
- ✗
Cloud ML Engine
Why it's wrong here
Cloud ML Engine is the retired predecessor of Vertex AI and offers no SQL syntax for training; models are built through Python or gcloud. It is tempting as a familiar name for hosted machine learning, and it would have been the right choice for training custom TensorFlow models via the legacy platform.
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This PMLE practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PMLE exam.