Generative AI Leader Practice Question: Business Strategies for Generative AI Solutions
What are THREE best practices for responsible generative AI deployment?
⚠ Common exam trap
Google Cloud often tests the misconception that bigger models are always better, but the trap here is that responsible AI deployment focuses on safety, fairness, and reliability rather than raw performance metrics like model size.
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
✓
Monitor model performance and data drift over time
Continuous monitoring of model performance and data drift is essential for maintaining the reliability and safety of generative AI systems. Data drift occurs when the statistical properties of input data change over time, which can degrade model accuracy and introduce unintended biases. Regular monitoring allows teams to detect these shifts early and retrain or adjust the model to sustain responsible behavior.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Monitor model performance and data drift over time
Why this is correct
Continuous monitoring helps detect degradation and ensures the model remains reliable.
- ✗
Maximize model size for best accuracy
Why it's wrong here
Larger models consume more resources and can amplify biases; size does not equate to responsibility.
- ✓
Maintain human oversight for critical decisions
Why this is correct
Human review ensures that automated decisions are reviewed, especially in high-stakes domains.
- ✓
Implement content filters to block harmful or biased outputs
Why this is correct
Content filters are a key safeguard against unintended generation.
- ✗
Avoid fine-tuning the model to preserve original capabilities
Why it's wrong here
Fine-tuning adjusts a model’s weights on domain-specific data to align outputs with organisational policies and safety guardrails; avoiding it leaves the model vulnerable to generating unconstrained or harmful content, which directly contradicts responsible deployment’s requirement for controlled output. This option is tempting because preserving a base model’s general capabilities is valuable for maintaining broad utility, and it would be correct in a scenario where the goal is to avoid catastrophic forgetting or maintain benchmark performance across diverse tasks.
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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.