Generative AI Leader Fundamentals of Generative AI Practice Question
A marketing team wants to generate product descriptions from a short list of features using a Google Cloud generative AI model. They have no labeled examples and want to avoid any model training. Which approach should they use?
⚠ Common exam trap
The trap here is assuming that any customization, such as fine-tuning, is required to get useful output from a foundation model.
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
✓
Zero-shot prompting with a foundation model in Vertex AI
Zero-shot prompting lets a pretrained foundation model perform a new task from instructions alone, with no examples or training. Since the team has no labeled data and wants to avoid training, this is the only approach that meets all constraints. Fine-tuning, training from scratch, and rule-based templates either require training or are not generative AI.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Training a new model from scratch using the team's feature list
Why it's wrong here
Training from scratch demands massive datasets, compute resources, and ML expertise, and it would not use the pretrained knowledge of a foundation model. The team's small feature list is insufficient for training. This approach is far too heavy for a simple text-generation task.
- ✗
Fine-tuning the model on a dataset of existing product descriptions
Why it's wrong here
Fine-tuning requires a labeled dataset of input-output pairs and additional training time and cost. The team has no labeled examples and explicitly wants to avoid training, so fine-tuning is unnecessary and would delay deployment. Zero-shot prompting achieves the goal without any training.
- ✓
Zero-shot prompting with a foundation model in Vertex AI
Why this is correct
Zero-shot prompting sends a task instruction and the feature list directly to a pretrained foundation model, which generates the description without any task-specific examples or training. This matches the team's need for immediate results with no labeled data and no model customization, making it the correct approach here.
- ✗
Using a traditional rule-based template system to assemble descriptions
Why it's wrong here
Rule-based templates are not generative AI and cannot produce varied, natural language. They also require manual template creation for each product type. The question asks for a generative AI approach, and templates do not leverage a foundation model's language capabilities.
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Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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