Generative AI Leader Fundamentals of Generative AI Practice Question
A data scientist needs to fine-tune a foundation model for a sentiment analysis task without managing infrastructure. Which Google Cloud service should they use?
⚠ Common exam trap
Watch out — candidates often confuse BigQuery ML's ability to train models on tabular data with the capability to fine-tune large language models, but BigQuery ML does not support fine-tuning of foundation models for NLP tasks.
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
✓
Vertex AI Model Garden
Vertex AI Model Garden is the correct service because it provides a curated hub of foundation models that can be fine-tuned with managed infrastructure, eliminating the need for the data scientist to provision or manage servers. It supports one-click deployment and fine-tuning workflows for sentiment analysis, directly addressing the requirement to avoid infrastructure management.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Compute Engine
Why it's wrong here
Compute Engine provides raw virtual machines, so the data scientist would still manage provisioning, drivers and scaling. It is tempting because Compute Engine offers full control over GPU hardware, and it would be the right choice when custom infrastructure or non-standard dependencies are genuinely required.
- ✗
BigQuery ML
Why it's wrong here
BigQuery ML trains models using SQL over warehouse tables, but it does not fine-tune foundation models for sentiment analysis. It is tempting because it is serverless and familiar to analysts, and would be the right choice for building traditional regression, classification or forecasting models directly on BigQuery data.
- ✗
Cloud Run
Why it's wrong here
Cloud Run hosts containerised applications and scales HTTP services, but it provides no fine-tuning mechanism for foundation models; the data scientist would still build and manage the training container. It is tempting as a serverless, infrastructure-free runtime, and would be correct for deploying a fine-tuned model as an inference endpoint.
- ✓
Vertex AI Model Garden
Why this is correct
Vertex AI Model Garden supplies foundation models with tuning workflows that run on managed infrastructure, so no cluster provisioning is needed. It directly satisfies the stem's constraint of fine-tuning for sentiment analysis without infrastructure management, unlike raw compute or self-hosted alternatives.
Go deeper
Related to this question
About these practice questions
This Generative AI Leader question is part of Courseiva's 1,008-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
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.