試験AB-100 トピック1 問題95 スレッド
Microsoft AB-100のリアル試験問題集
問題 #: 95
トピック #: 1
問題 #: 95
トピック #: 1
You are creating validation criteria for a custom generative AI model that produces business reports based on internal enterprise data.
You need to assess whether the model's outputs are appropriate and meaningful for the business reports.
Which metric should you use?
You need to assess whether the model's outputs are appropriate and meaningful for the business reports.
Which metric should you use?
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To validate a custom generative AI model for business reports based on internal data, you should focus on alignment with domain-specific tasks through a mix of automated and human-centric metrics.
Validation Criteria for Business Reports
*-> Task-Specific Quality Evaluation (TSQE): This is your primary metric for assessing whether outputs are meaningful for specific business tasks.
* Groundedness and Factuality: Measure the model's ability to provide information strictly referenced from your internal enterprise data. This prevents "hallucinations" that could lead to poor business decisions.
* Domain-Specific Benchmarking: Compare AI outputs against "ground truth" data-verified, accurate reports previously created by human experts.
Reference:
https://www.prompts.ai/blog/how-to-evaluate-generative-ai-llm-outputs-with-structure-and- precision
Validation Criteria for Business Reports
*-> Task-Specific Quality Evaluation (TSQE): This is your primary metric for assessing whether outputs are meaningful for specific business tasks.
* Groundedness and Factuality: Measure the model's ability to provide information strictly referenced from your internal enterprise data. This prevents "hallucinations" that could lead to poor business decisions.
* Domain-Specific Benchmarking: Compare AI outputs against "ground truth" data-verified, accurate reports previously created by human experts.
Reference:
https://www.prompts.ai/blog/how-to-evaluate-generative-ai-llm-outputs-with-structure-and- precision
Murayama 2026-06-09 06:10:54
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