Generative AI Leader Google Cloud's Generative AI Offerings Practice Question
A company is using Vertex AI Gemini API to analyze customer feedback. They notice that the model occasionally generates offensive content. They have already set safety settings to block high-probability harmful content. What additional step should they take to further reduce offensive outputs?
⚠ Common exam trap
Candidates often assume fine-tuning (Option D) is the default fix for any output quality issue, but safety filtering is a separate, configurable layer that should be tuned before retraining, and temperature (Option A) is often mistakenly thought to control safety when it only controls randomness.
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
✓
Adjust safety settings to block medium-probability harmful content
The company has already blocked high-probability harmful content, but offensive outputs can still occur at lower probability thresholds. By adjusting safety settings to block medium-probability harmful content, they tighten the filter to catch more borderline cases without requiring model retraining or sacrificing output diversity. This leverages Vertex AI's configurable safety filters, which operate on likelihood categories (e.g., high, medium, low) rather than just binary blocking.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Set the temperature to 0.0
Why it's wrong here
Temperature 0.0 makes sampling deterministic, selecting the highest-probability token; it does not remove offensive content already favoured by the model. It is tempting as a randomness control, and would be correct for reproducible, factual extraction tasks rather than safety filtering.
- ✓
Adjust safety settings to block medium-probability harmful content
Why this is correct
Safety settings operate on probability thresholds per harm category, so lowering the block threshold from high to medium catches harmful content the model would otherwise emit. This tightens filtering beyond the existing high-probability configuration, directly reducing offensive outputs from the Gemini API.
- ✗
Enable context caching
Why it's wrong here
Context caching reuses previously processed prompt prefixes to cut latency and token cost; it does not alter token probabilities or filter harmful output. It is tempting as a performance and cost optimisation, and would be correct for repeated long prompts, not for reducing offensive generations.
- ✗
Fine-tune the model on customer feedback data
Why it's wrong here
Fine-tuning on customer feedback data adapts style and domain vocabulary but does not reliably suppress harmful content, and could reinforce offensive patterns present in that data. It is tempting because it customises model behaviour, and would be correct for improving task-specific accuracy on labelled examples.
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Written by Johnson Ajibi, MSc IT Security
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