hardMultiple Choice
Generative AI Leader Practice Question: An enterprise is deploying a customer-facing…
An enterprise is deploying a customer-facing chatbot using a foundation model on Vertex AI. They need to ensure the model does not produce toxic outputs. Which combination of settings and features should they implement?
⚠ Common exam trap
Google often tests the distinction between training-time alignment techniques (like RLHF) and inference-time safety controls (like safety filters), tempting candidates to choose a fine-tuning approach when the question explicitly asks for deployment settings to prevent toxic outputs.
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
✓
Configure safety filters and safety settings in the model deployment to block harmful categories
Safety filters and safety settings in Vertex AI model deployment are the direct mechanism to block harmful categories of output at inference time. These settings allow administrators to define thresholds for categories like toxicity, harassment, and hate speech, ensuring the model refuses to generate prohibited content without requiring retraining or post-hoc monitoring.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use Reinforcement Learning from Human Feedback (RLHF) during fine-tuning
Why it's wrong here
RLHF shapes overall model behaviour during training, but it cannot guarantee runtime filtering of toxic outputs for a deployed chatbot. It is tempting because RLHF genuinely aligns models, yet the scenario needs Vertex AI's safety filters and threshold settings applied at inference time.
- ✗
Enable Vertex AI Model Monitoring and set up alerts for toxic outputs
Why it's wrong here
Model Monitoring detects drift and anomalies in deployed models and raises alerts after the fact; it does not block a toxic response reaching the user. Monitoring suits ongoing quality oversight, whereas preventing toxic output requires inference-time safety filters and configured thresholds.
- ✗
Reduce the temperature to 0.0 and increase top-k to 50
Why it's wrong here
Temperature and top-k govern randomness and token sampling breadth, not content safety; lowering temperature makes outputs more deterministic but can still emit toxic text. These parameters suit controlling creativity, while toxicity prevention needs Vertex AI safety filters with configured thresholds.
- ✓
Configure safety filters and safety settings in the model deployment to block harmful categories
Why this is correct
Safety filters and configurable safety settings block harmful content categories such as harassment, hate speech and dangerous material before responses reach users. Applied at the model deployment layer, they satisfy the requirement to prevent toxic outputs in a customer-facing chatbot without altering the underlying foundation model.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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