SnowPro Specialty: Gen AI Question 254
Single answerCortex AnalystA retail company is building a self-service analytics chatbot for business users. They want users to ask questions such as "What were online shoe sales in the Northeast last quarter?" and have Snowflake generate trustworthy SQL against governed data. The data team has already modeled the warehouse tables, but early tests show the chatbot sometimes chooses the wrong join path and misinterprets business terms like "online sales" and "Northeast." Which action should the team take to improve accuracy when using Cortex Analyst?
- A
Create and refine a semantic model that defines metrics, dimensions, relationships, and business synonyms, then have Cortex Analyst use that model for natural-language-to-SQL generation.
- B
Replace the warehouse tables with vector embeddings so Cortex Analyst can retrieve the nearest business meaning before generating SQL.
- C
Fine-tune a custom large language model inside Snowflake on historical BI dashboards so Cortex Analyst learns company terminology automatically.
- D
Grant Cortex Analyst OWNER privileges on the underlying schemas so it can inspect all objects and infer the intended joins at runtime.
Show answer and explanation
Correct answer: A
Explanation
Cortex Analyst is intended for natural-language analytics over structured enterprise data, and its accuracy depends on a well-defined semantic model. In practice, teams improve results by explicitly modeling business concepts such as measures, dimensions, relationships, filters, and synonyms so that natural-language questions can be translated into correct SQL. This is especially important when terms are ambiguous or multiple join paths exist. Best practice is to use governed semantic definitions rather than relying on broad privileges, embeddings, or ad hoc model training. Refer to Snowflake documentation for Cortex Analyst and semantic modeling guidance, which emphasizes defining business meaning and relational context to improve text-to-SQL reliability.
- A. Correct.
Correct. Cortex Analyst is designed to generate SQL from natural language using a semantic model that captures business meaning and governed analytical structure. Defining metrics, dimensions, relationships, and synonyms helps the service map phrases like "online sales" or "Northeast" to the correct fields and filters, and it reduces errors caused by ambiguous joins or inconsistent terminology. This is the recommended approach for trustworthy text-to-SQL over enterprise data.
- B. Incorrect.
Incorrect. Vector embeddings are useful for semantic search and retrieval scenarios, but they do not replace the governed relational semantics needed for accurate SQL generation in Cortex Analyst. The problem described is not primarily document retrieval; it is analytical interpretation of business definitions and joins. Using embeddings instead of a semantic model would not solve the core issue.
- C. Incorrect.
Incorrect. The scenario does not require model fine-tuning, and Cortex Analyst accuracy is improved through semantic modeling rather than custom LLM fine-tuning on BI artifacts. A common misconception is that every domain-specific language problem should be solved by training a model, but Snowflake's intended pattern for Cortex Analyst is to encode business context in the semantic layer.
- D. Incorrect.
Incorrect. Excessive privileges do not improve semantic understanding. Cortex Analyst still needs governed definitions of metrics, dimensions, and relationships to produce reliable SQL. Granting broad ownership rights would create unnecessary security risk and does not address the root cause of incorrect join selection or business-term ambiguity.