SnowPro Specialty: Gen AI Question 253
Single answerCortex AnalystA retail analytics team is building a natural-language analytics assistant for business users. They want users to ask questions such as "What were online sales in the Northeast last quarter?" and have Snowflake Cortex Analyst generate accurate SQL against curated warehouse data. The team has one semantic model per business domain and wants to reduce incorrect joins and ambiguous metric definitions before exposing the assistant broadly. Which action is the BEST next step to improve answer quality in Cortex Analyst?
- A
Define and refine the semantic model so metrics, dimensions, relationships, and business-friendly synonyms are explicitly described for the domain
- B
Increase the size of the virtual warehouse used by Cortex Analyst so the model has more compute to reason about ambiguous business terms
- C
Convert the semantic model into unstructured documents and store them in a stage so Cortex Analyst can infer joins from document context
- D
Create dynamic tables for every source table because Cortex Analyst requires dynamic tables to understand time-based aggregations
Show answer and explanation
Correct answer: A
Explanation
For Cortex Analyst, the semantic model is the core mechanism that grounds natural-language questions in trusted analytical logic. Best practice is to curate domain-specific semantic models with clearly defined metrics, dimensions, relationships, and user-friendly terminology so Analyst can generate accurate SQL and avoid ambiguous interpretations. This is especially important when multiple tables could be joined in different ways or when business terms such as "sales," "revenue," or "region" have specific approved definitions. In Snowflake documentation and product guidance, Cortex Analyst is positioned as a semantic-model-driven analytics experience rather than a generic document-retrieval workflow. Therefore, improving the semantic model is the most direct and effective action to improve answer quality.
- A. Correct.
Correct. Cortex Analyst is grounded in a semantic model that describes the business meaning of data, including metrics, dimensions, relationships, and synonyms. When users ask natural-language questions, the quality of generated SQL depends heavily on how well this semantic model captures the intended business logic. Refining the semantic model is the most effective way to reduce incorrect joins, resolve ambiguity, and improve consistency across domains.
- B. Incorrect.
Incorrect. Warehouse size affects query execution performance, not the semantic understanding used by Cortex Analyst to map a user question to the right SQL. If the issue is ambiguous terms, inconsistent definitions, or poor join selection, adding compute will not fix the underlying semantic modeling problem.
- C. Incorrect.
Incorrect. Cortex Analyst is designed to work from a structured semantic model for analytics use cases, not by inferring relational logic from unstructured documents in a stage. Storing documentation or table descriptions as files may help human readers, but it does not replace the explicit semantic definitions Analyst uses to generate reliable SQL.
- D. Incorrect.
Incorrect. Dynamic tables can be useful for data preparation pipelines, but they are not a requirement for Cortex Analyst. They do not inherently tell Analyst which metrics to use, how business terms map to columns, or which joins are valid. The misconception is confusing data freshness or transformation features with semantic-layer design.