SnowPro Advanced: Security Engineer Question 421
Single answerUse LLM-as-a-Judge to evaluate AI application responses for bias, toxicity, and accuracy (relevant to data security and responsible AI)A financial services company built a customer-support assistant in Snowflake that uses Cortex AI to generate answers from internal policy documents and ticket history. The security engineering team must add an automated evaluation step that uses an LLM-as-a-Judge to score each response for toxicity, bias, and factual accuracy before responses are logged for review. Because prompts and model outputs may contain sensitive customer data, the team wants an approach aligned with Snowflake security best practices and responsible AI controls. Which solution BEST meets these requirements?
- A
Create a secured evaluation pipeline in Snowflake that stores prompts, retrieved context, model responses, and judge scores in controlled tables; restrict access with RBAC and masking policies where needed; and use an LLM-as-a-Judge prompt that compares the response to approved context to score toxicity, bias, and groundedness/accuracy.
- B
Export all prompts and model responses nightly to an external SaaS spreadsheet so human reviewers can label toxicity and bias manually, because LLM-as-a-Judge is not appropriate for production evaluation of sensitive responses.
- C
Use only the application model's confidence score as the evaluation metric, because a high-confidence response is sufficient evidence that the answer is accurate and non-toxic.
- D
Run the judge model directly on raw production prompts and responses without storing any evaluation artifacts, because avoiding storage removes the need for Snowflake access controls and reduces security risk.
Show answer and explanation
Correct answer: A
Explanation
The best answer is Option 1 because it combines responsible AI evaluation with core Snowflake security controls. In a SnowPro Advanced: Security Engineer context, the candidate should recognize that LLM-as-a-Judge is most useful when implemented as a governed evaluation pipeline: keep sensitive prompts and outputs inside Snowflake where possible, protect them with role-based access control, and apply masking or similar policy controls to reduce unnecessary exposure. For accuracy, the judge should evaluate whether the answer is grounded in approved source context rather than relying on self-reported confidence. For bias and toxicity, the judge should use explicit evaluation criteria and structured scoring. This design also preserves auditability for compliance and incident response. Relevant Snowflake best practices include using RBAC for least-privilege access, applying data protection policies to sensitive fields, and centralizing governed data processing rather than exporting sensitive AI artifacts to less controlled platforms.
- A. Correct.
Correct. This option applies LLM-as-a-Judge in a way that is operationally realistic and aligned with Snowflake security practices. Storing prompts, retrieved evidence, model outputs, and judge results in Snowflake allows controlled, auditable evaluation workflows. RBAC limits who can read evaluation data, and masking policies can protect sensitive fields in logs. Comparing the generated answer against approved retrieved context is the right pattern for assessing groundedness or factual accuracy, while separate criteria can be used for toxicity and bias. This supports responsible AI review without unnecessarily moving sensitive data outside Snowflake.
- B. Incorrect.
Incorrect. Human review can be valuable for spot checks and calibration, but exporting sensitive prompts and responses to a spreadsheet-based external tool is not the best security approach. It increases data exposure and weakens centralized governance. Also, the statement that LLM-as-a-Judge is not appropriate for production evaluation is too broad; in practice, it is commonly used for scalable automated evaluation, often supplemented with human review.
- C. Incorrect.
Incorrect. Model confidence is not a reliable substitute for safety or factuality evaluation. A model can be highly confident and still produce biased, toxic, or hallucinated content. Responsible AI evaluation requires explicit checks against those dimensions rather than inferring them from confidence alone.
- D. Incorrect.
Incorrect. Not storing any evaluation artifacts reduces auditability and makes it difficult to investigate incidents, validate controls, or tune the evaluation framework. It also does not eliminate the need for security controls, because sensitive data is still being processed. Snowflake best practices emphasize governed access, auditability, and policy-based protection rather than avoiding storage altogether.