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Snowflake SnowPro® Specialty: Gen AI Certification Exam Sample Questions (Q302-Q307):

NEW QUESTION # 302
A data engineering team is implementing a Document AI solution to automate the extraction of vendor invoice details. They have already published a Document AI model build, 'vendor_invoice_model', located in New invoices are uploaded to an internal stage, 'fin db.invoice_processing_schema.raw invoices_stage'. The 'invoice_pipeline role' is responsible for executing the
! PREDICT
method on these staged documents as part of an automated task. Which of the following USAGE privileges are essential for to successfully execute the
! PREDICT
method and process documents from the specified stage, assuming the required SNOWFLAKE.DOCUMENT_INTELLIGENCE_CREATOR role is already granted?

Answer: A,B,C

Explanation:


NEW QUESTION # 303
A data governance team is concerned about the consistency and compliance of SQL queries generated by Cortex Analyst for sensitive financial reporting. They need to ensure that all generated SQL for a specific semantic model always includes a 'WHERE' clause that filters data for 'region = 'EMEA" and adheres to 'ISO 8601' date formatting for all date columns, regardless of the user's natural language input. Which of the following approaches is the MOST effective for implementing these strict, overarching requirements within Cortex Analyst's semantic model?

Answer: A

Explanation:
To enforce overarching, consistent behavior in SQL query generation regardless of user input, the 'custom_instructions' field in the semantic model YAML is the most appropriate mechanism. It allows providing unique business context and directives directly to the LLM that Cortex Analyst uses, enabling greater control over the generated SQL. Option A (verified queries) is good for specific questions but would require an exhaustive and potentially unmanageable list to cover *all* user inputs for general rules. Option C (logical table-level filters) can apply 'WHERES' clauses but doesn't inherently enforce a specific date format across all date columns consistently without further explicit definition for each time dimension, and the source doesn't detail a filter mechanism for global date format enforcement. Option D ('task_description' in "CLASSIFY _ TEXT) is for task-specific classification and not for controlling SQL generation by Cortex Analyst. Option E (custom UDFs for post- processing) is an external step to Cortex Analyst's core text-to-SQL generation and adds unnecessary complexity, losing the benefit of Cortex Analyst's managed nature, and is not a direct configuration *within* Cortex Analyst to influence SQL generation.


NEW QUESTION # 304
A data team is refining their Cortex Analyst semantic model to improve the accuracy of responses for specific, frequently asked questions and to enable better literal value searches. Consider a semantic model being developed to address these requirements. Which two configurations or features are directly relevant and correctly applied in the semantic model YAML for these purposes?

Answer: A,E

Explanation:
Option A is correct. Cortex Search Services can be integrated into a dimension's definition (using the field with 'service' and fields) to improve literal matching by performing semantic search over the underlying column, which enhances Cortex Analyst's ability to find literal values for filtering. Option B is correct. The 'verified_querieS section allows pre-defining accurate SQL queries for specific natural language questions. Setting 'use_as_onboarding_question true' ensures these queries are used when relevant and presented as suggested questions to users. Option C is incorrect; while metrics can reference logical columns, 'relationships' between logical tables are necessary for defining joins, especially across different underlying base tables. Option D is incorrect; 'custom_instructions' are provided at the model level to give general context to the LLM for SQL query generation, not embedded within individual dimension definitions. Option E is incorrect; the 'sample_valueS field is recommended for dimensions with relatively low-cardinality (approximately 1-10 distinct values) to aid in semantic search for literals, not for high-cardinality dimensions.


NEW QUESTION # 305
A Snowflake team observes consistently high token costs from 'SNOWFLAKE.ACCOUNT_USAGE.CORTEX_FUNCTIONS_QUERY_USAGE_HISTORY' for a summarization task using the 'mistral- large2' model. The task involves summarizing legal documents, which often exceed the context window of common LLMs. To optimize these token-based costs, which strategy should the team prioritize?

Answer: C

Explanation:
Option C is correct. For summarization of lengthy documents, exceeding the context window or using large inputs significantly increases token consumption. Text splitting, for example using , can break documents into smaller, more manageable chunks. This reduces the number of input tokens per LLM call, directly leading to cost optimization, and is recommended for best search results and LLM response quality with Cortex Search. Option A is incorrect because for Cortex AISQL functions, Snowflake recommends using a smaller warehouse (no larger than MEDIUM) as larger warehouses do not increase performance but can result in unnecessary costs associated with keeping the warehouse active. The compute cost for Cortex LLM functions is based on tokens processed, not warehouse size performance. Option B is incorrect because 'TRY COMPLETE only prevents costs for 'failed' operations by returning NULL instead of an error. It does not optimize the token consumption of 'successful' summarization tasks. Option D is incorrect; Cortex Guard processes additional tokens for its filtering, thus 'increasing' token consumption, not reducing it. Option E is incorrect because setting 'temperature' to 0 makes the output more deterministic, which might improve consistency but does not directly reduce the number of input or output tokens processed for a summarization task.


NEW QUESTION # 306
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: C,D

Explanation:


NEW QUESTION # 307
......

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