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Generative AI Leader Practice Question: A social media company uses a generative AI to…
A social media company uses a generative AI to moderate user comments. They need to filter hate speech, violence, and sexual content. What is the most efficient way to implement content safety in Vertex AI?
⚠ Common exam trap
The Generative AI Leader exam often tests the misconception that custom training (AutoML) is always better for domain-specific tasks, but here the pre-built filters are already optimized for the exact content categories needed, making custom training unnecessary and inefficient.
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
✓
Use Google's pre-built safety filters provided with Vertex AI
Google's pre-built safety filters in Vertex AI are specifically designed for content moderation tasks like hate speech, violence, and sexual content detection. They are immediately available, require no custom training, and integrate directly with Vertex AI's generative AI workflows, making them the most efficient choice for a social media company needing rapid deployment.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Hire human moderators to manually review all comments
Why it's wrong here
Manual review cannot scale to the full comment volume and introduces latency and cost that automated classification avoids. Vertex AI's built-in safety filters and configurable harm thresholds apply hate, violence and sexual categories at inference time. Human moderators suit appeals or edge-case adjudication, not first-pass filtering of every comment.
- ✗
Use a third-party API for content moderation
Why it's wrong here
A third-party API adds an external dependency, extra latency and data-egress concerns, and its category taxonomy will not align with Vertex AI's harm thresholds. Vertex AI provides native safety filters covering hate speech, violence and sexual content. A third-party service fits when moderating content outside Google Cloud entirely.
- ✗
Train a custom content classifier from scratch using Vertex AI AutoML
Why it's wrong here
Training a custom classifier from scratch demands labelled data, compute and tuning time, and duplicates categories Vertex AI already detects. Native safety filters apply hate, violence and sexual thresholds without training. AutoML is warranted when the required categories are domain-specific and absent from the built-in filter set.
- ✓
Use Google's pre-built safety filters provided with Vertex AI
Why this is correct
Vertex AI's pre-built safety filters apply configurable thresholds across harm categories including hate speech, violence and sexual content, requiring no custom model training. This satisfies the efficiency requirement: the filters operate on requests and responses directly, giving immediate coverage rather than building and maintaining bespoke classifiers.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This Generative AI Leader practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the Generative AI Leader exam.