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NEW QUESTION # 79
A business intelligence team wants to enable non-technical users to query structured data in Snowflake using natural language. They are considering Cortex Analyst. What is the primary role of a semantic model in Cortex Analyst to achieve this goal for structured/text-to-SQL use cases?
Answer: A
Explanation:
Option C is correct. Cortex Analyst uses semantic models to bridge the gap between business users' natural language and the technical database schema. Semantic models provide semantic information like descriptive names and synonyms for tables and columns, which helps the underlying LLM accurately generate SQL queries from natural language questions. Option A is incorrect because the semantic model does not directly execute SQL; it provides the context for an LLM to generate SQL. Option B is incorrect as access control is managed by Snowflake's RBAC and not stored within the semantic model itself. Option D is incorrect; while performance is a consideration, caching is not the primary role of the semantic model in bridging the language gap for text-to-SQL functionality. Option E is incorrect because while vector embeddings are used in Snowflake (e.g., Cortex Search for RAG), the semantic model itself isn't primarily a vector store for all data columns for direct semantic search in this context; rather, it provides metadata for text-to-SQL generation.
NEW QUESTION # 80
An analytics team is preparing documents for a new Document AI model build to extract information from internal policy reviews. They have a variety of documents that they intend to upload to an internal stage for processing. The document list includes: (1 ) a 70 MB PDF with 100 pages, (2) a 45 MB DOCX with 150 pages, (3) a 30 MB PNG image, (4) a 60 MB TIFF image, and (5) a 20 MB HTML file. All documents are in English. Which of these documents would 'fail' to meet the direct input requirements for Document AI processing?
Answer: C,D,E
Explanation:
NEW QUESTION # 81
A data application developer is building a Streamlit chat application within Snowflake. This application uses a RAG pattern to answer user questions about a knowledge base, leveraging a Cortex Search Service for retrieval and an LLM for generating responses. The developer wants to ensure responses are relevant, concise, and structured. Which of the following practices are crucial when integrating Cortex Search with Snowflake Cortex LLM functions like AI_COMPLETE for this RAG chatbot?
Answer: A,D
Explanation:
NEW QUESTION # 82
An administrator has configured the 'CORTEX_MODELS_ALLOWLIST parameter to only permit the 'mistral-large? model at the account level. A user with the 'PUBLIC' role, which has been granted 'SNOWFLAKE.CORTEX USER and 'SNOWFLAKE."CORTEX- MODEL-ROLE-LLAMA3.1-70B"' , attempts to execute several 'AI COMPLETE queries. Which of the following queries will successfully execute?
Answer: C,E
Explanation:
Option A is correct. The query directly references 'MISTRAL-LARGE2', which is explicitly in the account-level , so it will succeed. Option B is correct. When a model name is provided as a string argument, Cortex first treats it as an identifier for a schema-level model object. If found, RBAC is applied. The user's role has 'SNOWFLAKE."CORTEX-MODEL-ROLE-LLAMA3.1-70B"' granted, which provides access to the 'LLAMA3.1-70B' model object in 'SNOWFLAKE.MODELS' , regardless of the 'CORTEX MODELS_ALLOWLIST setting for plain model names. Option C is incorrect because 'llama3.1-70b' as a plain model name is not in the 'CORTEX_MODELS_ALLOWLIST. Although the user has access to the model object, a plain string like 'llama3.1-70b' will be looked up in the allowlist after failing to match a model object by that plain name, and the allowlist only has 'MISTRAL-LARGE2'. Option D is incorrect. 'snowflake-arctic' is neither in the 'CORTEX MODELS ALLOWLIST nor does the user have a specific application role granting access to a 'snowflake-arctic' model object. Option E is incorrect because 'ALTER ACCOUNT operations can only be performed by the 'ACCOUNTADMIN' role, not by a SPUBLIC' user role.
NEW QUESTION # 83
A Gen AI developer has a Document AI pipeline that uses a query with 'GET PRESIGNED URL' to process multi-page PDF documents. Despite the internal stage being correctly set up with 'SNOWFLAKE SSE' encryption and the model build being published, they observe inconsistent results. Some documents result in a Received HTTP 403 response for presigned URL. URL may be expired.
error, while other documents (containing complex diagrams and dense text in an unsupported language like Korean) are processed, but the extracted information is often incomplete or inaccurate.
Which two factors are most likely contributing to these observed issues?
Answer: B,C
Explanation:
The error 'Received HTTP 403 response for presigned URL. URL may be expired.' directly indicates that the function's default expiration time is causing some documents to be inaccessible by the Document AI model. This is a common issue when processing pipelines encounter delays. Additionally, the observation of incomplete or inaccurate extraction for documents with 'dense text in an unsupported language like Korean' directly points to language limitations. Document AI explicitly lists supported languages (English, Spanish, French, German, Portuguese, Italian, and Polish) and states that results for other languages might not be satisfactory. While the question mentions 'multi-page PDF documents' without explicitly stating they exceed page limits, the mention of 'complex diagrams and dense text' can also imply potential issues if page length (max 125 pages) is exceeded or other document requirements are not met. Thus, option D comprehensively covers these content- related issues. Option A (outdated model version) is unlikely to cause these specific errors, as the latest model is used by default if not specified. Option C (missing 'EXECUTE TASK privilege) would prevent task execution entirely, not cause intermittent URL issues or content- specific extraction problems. Option E (answers exceeding token limits) would be reflected in truncated output, not necessarily 'incomplete or inaccurate' extraction in the sense of failing to identify information in the first place.
NEW QUESTION # 84
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