Generative AI Leader Google Cloud's Generative AI Offerings Practice Question
A developer is using Vertex AI's Generative AI Studio to prototype a text summarization model. The initial results are too verbose. What is the most efficient way to adjust the output length without retraining?
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
✓
Modify the prompt with specific length instructions and adjust model parameters
The most efficient way to adjust output length without retraining is to modify the prompt with specific length instructions (e.g., 'Summarize in one sentence') and adjust model parameters like max output tokens, temperature, and top_p in Vertex AI's Generative AI Studio. Option A is wrong because switching to a smaller base model like BERT would require retraining or different model architecture, and BERT is not primarily a text generation model for summarization. Option B is wrong because adding a separate classifier increases complexity and does not directly control the model's output length. Option C is wrong because fine-tuning requires additional training data and computational resources, which is not the most efficient approach compared to prompt and parameter adjustments.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Switch to a smaller base model like BERT
Why it's wrong here
BERT is an encoder producing embeddings and classification outputs, not a generative summariser, so swapping to it removes the summarisation capability entirely. Verbosity is fixed by lowering max output tokens in Generative AI Studio. Smaller encoder models suit tasks such as sentiment analysis or named-entity recognition.
- ✗
Use a separate classifier to filter long responses
Why it's wrong here
A separate classifier filters responses after generation, so the model still produces verbose text and the summarisation output is discarded rather than shortened. Generation length is controlled by the max output tokens parameter. Classifiers suit routing or content moderation decisions, not constraining a model's token budget.
- ✗
Fine-tune the model with a dataset of concise summaries
Why it's wrong here
Fine-tuning alters model weights through a training job, which the stem explicitly excludes, and it is costly for a prototype. Output length is governed at inference time by the max output tokens parameter in Generative AI Studio. Fine-tuning would be correct when the goal is teaching domain style or format, not trimming verbosity.
- ✓
Modify the prompt with specific length instructions and adjust model parameters
Why this is correct
Modifying the prompt with explicit length instructions directly constrains generation at inference time, while adjusting parameters such as max output tokens and temperature tightens verbosity further. Both act without retraining, satisfying the stem's efficiency constraint, since no fine-tuning or dataset preparation is required to shorten summaries in Generative AI Studio.
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