AIF-C01 Fundamentals of Generative AI Practice Question
Which TWO practices help ensure responsible AI when deploying generative AI applications? (Select TWO.)
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
✓
Implement guardrails to filter harmful or inappropriate content
Implementing guardrails (e.g., content filtering) to prevent harmful outputs (D) and continuously monitoring outputs for bias and drift (E) are essential responsible AI practices. Deploying without filters (A) is unsafe, increasing model size (B) reduces interpretability and doesn't address responsibility, and using only synthetic data (C) can introduce bias and is impractical. The correct answers are D and E.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Deploy the model without any content filters to maximize creativity
Why it's wrong here
Lack of filters can lead to harmful outputs; deploying without safeguards is irresponsible.
- ✗
Increase model size to improve accuracy at the expense of interpretability
Why it's wrong here
Larger models are less interpretable and do not inherently improve responsibility; accuracy is not the only goal.
- ✗
Use only synthetic data for training to avoid privacy issues
Why it's wrong here
Synthetic data may not represent real-world scenarios and can introduce bias. Responsible AI includes transparent data sourcing.
- ✓
Implement guardrails to filter harmful or inappropriate content
Why this is correct
Guardrails like Amazon Bedrock Guardrails help enforce content policies and prevent harmful outputs.
- ✓
Monitor the model's outputs for bias and drift over time
Why this is correct
Continuous monitoring allows detection of bias and performance degradation, enabling corrective action.
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