Generative AI Leader Google Cloud's Generative AI Offerings Practice Question
You are a generative AI architect for a large e-commerce company. Your team has built a product description generator using Vertex AI's text-bison model. The model is accessed via the Vertex AI API from a web application. You have set the temperature to 0.5 and top_k to 40. The team reports that the generated descriptions are often too generic and lack creativity. They want the descriptions to be more diverse and engaging. You are also concerned about cost, as each API call is billed. Which change should you recommend to increase creativity while managing cost?
⚠ Common exam trap
Google Cloud often tests the misconception that increasing creativity requires a larger model or more expensive resources, when in fact tuning sampling parameters like temperature and top_k is the correct, cost-neutral approach.
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
✓
Increase the temperature to 0.8 and keep top_k at 40.
Increasing the temperature to 0.8 makes the model's output probability distribution flatter, which increases randomness and allows less likely tokens to be selected. This directly addresses the need for more diverse and creative descriptions. Keeping top_k at 40 ensures the model still considers a broad set of candidate tokens, balancing creativity with coherence, and does not increase API call costs since temperature and top_k are inference parameters that do not affect billing.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Keep temperature at 0.5 but reduce top_k to 20.
Why it's wrong here
Lowering top_k to 20 narrows sampling to the twenty highest-probability tokens, reducing diversity and making output blander, not more creative. top_k is tempting because it does control randomness, but raising it widens the candidate pool; the stem needs higher temperature and top_k, not a tighter cutoff.
- ✓
Increase the temperature to 0.8 and keep top_k at 40.
Why this is correct
Raising temperature to 0.8 flattens the probability distribution, so lower-probability tokens are sampled more often, producing more diverse and engaging descriptions. Keeping top_k at 40 preserves the existing candidate pool and call volume, so Vertex AI API billing per call stays unchanged.
- ✗
Switch to a larger model like text-bison@002 and keep same parameters.
Why it's wrong here
Swapping to text-bison@002 changes the model version, not the sampling behaviour; identical temperature and top_k values still yield similarly generic phrasing while larger models typically raise per-call cost. Model selection is tempting when quality is poor, but creativity here is governed by decoding parameters, and the stem also demands cost control.
- ✗
Decrease the temperature to 0.2 and increase top_k to 60.
Why it's wrong here
Dropping temperature to 0.2 sharpens the probability distribution toward the likeliest tokens, producing safer, more repetitive text; raising top_k to 60 widens candidates but cannot offset that. Temperature is tempting as the creativity dial, yet the stem requires increasing it, not decreasing it, to diversify descriptions.
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