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Generative AI Leader Practice Question: A data science team wants to run a machine…
A data science team wants to run a machine learning model directly on data stored in BigQuery without moving the data to a separate environment. Which Google Cloud service should they use?
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 enables creating and executing ML models using standard SQL queries directly on data in BigQuery, eliminating data movement.
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 Training
Why it's wrong here
Vertex AI Training builds and trains custom models in its own managed environment, requiring data to be exported or accessed from BigQuery rather than executing inside it. It is tempting because Vertex AI is Google Cloud's flagship ML platform, but it is correct for custom training pipelines, not for running models directly on BigQuery data.
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Cloud Dataproc
Why it's wrong here
Cloud Dataproc runs Spark and Hadoop clusters, which require copying or reading BigQuery data into cluster storage rather than executing the model in place. It is tempting because Dataproc handles large-scale distributed processing, but it is the right choice for migrating existing Spark or Hadoop workloads, not for in-database BigQuery ML.
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Google AI Studio
Why it's wrong here
Google AI Studio is a browser-based environment for prototyping prompts and Gemini models, and it cannot execute training or inference directly against BigQuery tables. It is tempting because it offers quick model experimentation, but it is the correct choice for prompt design and API key generation, not in-database machine learning.
- ✓
BigQuery ML
Why this is correct
BigQuery ML lets the team create and run machine learning models using SQL directly against data held in BigQuery, so no extraction or movement to a separate environment occurs. This satisfies the constraint of training without relocating the data.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This Generative AI Leader 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 Generative AI Leader exam.