AWS Certified Cloud Practitioner

Explanation

Correct Answer: D. Benchmark datasets

Why Benchmark Datasets are the best choice:

1. Pre-existing and standardized: Benchmark datasets are specifically designed and curated for evaluating AI models, including LLMs, for bias and fairness. They are readily available and don't require the company to create their own evaluation data.

2. Minimal administrative effort: Using benchmark datasets requires the least administrative effort because:

   • No need to collect and clean new data
   • No need to annotate or label data
   • No need to create evaluation frameworks from scratch
   • The datasets come with established evaluation metrics and methodologies

3. Comprehensive bias evaluation: Benchmark datasets are specifically designed to test for various types of bias and discrimination across different demographic groups, making them ideal for the company's purpose.

Why the other options are not optimal:

• A. User-generated content: This would require significant administrative effort to collect, clean, annotate, and prepare for evaluation. It's not standardized and may not cover all the bias scenarios the company wants to test.

• B. Moderation logs: While these contain historical moderation decisions, they would need extensive processing to extract relevant patterns and may reflect existing biases in the current moderation system.

• C. Content moderation guidelines: These are policy documents, not data sources for evaluating LLM outputs. They could inform what to look for but don't provide actual data for testing.

Key Takeaway: When evaluating AI models for bias and fairness, benchmark datasets provide the most efficient and standardized approach with minimal administrative overhead, as they are specifically designed for this purpose and come with established evaluation protocols.