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Snowflake SnowPro® Specialty: Gen AI Certification Exam Sample Questions (Q128-Q133):

NEW QUESTION # 128
A data engineering team is designing a Snowflake data pipeline to automatically enrich a 'customer issues' table with product names extracted from raw text-based 'issue_description' columns. They want to use a Snowflake Cortex function for this extraction and integrate it into a stream and task-based pipeline. Given the 'customer_issues' table with an 'issue_id' and (VARCHAR), which of the following SQL snippets correctly demonstrates the use of a Snowflake Cortex function for this data enrichment within a task, assuming is a stream on the 'customer issues' table?

Answer: D

Explanation:
Option B correctly uses to pull specific information (product name) from unstructured text, which is a common data enrichment task. It also integrates with a stream ('issue_stream') by filtering for 'METADATA$ACTION = 'INSERT" and uses a 'MERGE statement, which is suitable for incremental updates in a data pipeline by inserting new extracted data based on new records in the stream. Option A uses for generating a response, not for specific entity extraction, and its prompt is less precise for this task than 'EXTRACT_ANSWER. Option C uses 'SNOWFLAKE.CORTEX.CLASSIFY_TEXT for classification, not direct entity extraction of a product name, and attempts to update the source table directly, which is not ideal for adding new columns based on stream data. Option D proposes a stored procedure and task, which is a valid pipeline structure. However, the EXTRACT ANSWER call within the procedure only returns a result set and does not demonstrate the final insertion or merging step required to persist the extracted data into an 'enriched_issues' table. Option E uses to generate vector embeddings, which is a form of data enrichment, but the scenario specifically asks for 'product names' (a string value), not embeddings for similarity search.


NEW QUESTION # 129
A developer is instrumenting a RAG application using the TruLens SDK within Snowflake AI Observability. The application has distinct functions for retrieving context and generating a completion. To ensure clear tracing and readability, which span_type should ideally be used for the function responsible for retrieving relevant text from the vector store?

Answer: C

Explanation:
The TruLens SDK allows for specifying span_type to improve the readability and understanding of traces. For a RAG application, RETRIEVAL the span type is explicitly recommended for search services or retrievers (functions that retrieve context). GENERATION is used for LLM inference calls that generate answers, and RECORD_ROOT identifies the entry point method of the application.


NEW QUESTION # 130
A Snowflake developer, named ANALYST USER, is tasked with creating a Streamlit in Snowflake (SiS) application that will utilize both SNOWFLAKE. CORTEX. COMPLETE for generating responses and SNOWFLAKE. CORTEX.CLASSIFY_TEXT for categorizing user input. To ensure the role used by ANALYST USER has the necessary permissions for executing these Cortex LLM functions and operating within a specified database and schema, which of the following database roles or privileges must be granted? (Select all that apply.)

Answer: A,C

Explanation:
To execute Snowflake Cortex AI functions such as 'SNOWFLAKE.CORTEX.COMPLETE and 'SNOWFLAKE.CORTEX.CLASSIFY_TEXT , the role used by the developer must be granted the 'SNOWFLAKE.CORTEX_USER database role. This role provides the necessary permissions to call these specific AI functions. Additionally, for a Streamlit application to run and perform operations within a designated database and schema (e.g., accessing tables, stages, or storing outputs), the role requires the 'USAGE privilege on that database and schema. Option B ('CREATE SNOWFLAKE.ML.DOCUMENT INTELLIGENCE') is a privilege specifically for creating DocumentAI model builds, not for using general Cortex LLM functions. Option D (EXECUTE TASK) is required for creating and running tasks, typically in automated data pipelines, which is distinct from direct LLM function execution within a Streamlit app. Option E is an application role necessary for AI Observability to log and view application traces for debugging and performance evaluation, but it is not a core requirement for merely executing LLM functions.


NEW QUESTION # 131
A data engineer is tasked with defining a semantic model for Cortex Analyst to enable natural language queries over sales dat a. They are creating a YAML file to describe the logical structure. Which of the following statements correctly describe the configuration of this semantic model? (Select all that apply)

Answer: A,B,E

Explanation:
Option A is correct. A logical table, a foundational concept of Snowflake's semantic model, represents either a physical database table or a view and its 'base_table' field specifies the fully qualified name of the underlying physical table. Option B is correct. Dimensions can specify a block to integrate with Cortex Search, and the 'literal_column' field within this block is optional and defaults to the search index. Option C is incorrect. The 'VARIANT, 'OBJECT, GEOGRAPHY' , and 'ARRAY' data types are explicitly not supported for dimensions, time dimensions, or facts within a semantic model. Option D is incorrect. Verified queries must use the names of the logical tables and columns as defined in the semantic model, not those in the underlying physical dataset. For example, sales_data' for a logical table named 'sales_data'. Option E is correct. Metrics can be defined using an SQL expression ('expr') that can reference logical columns (facts, dimensions, or time dimensions) within the same logical table or from another logical table in the semantic model.


NEW QUESTION # 132
A data engineering team needs to configure their Snowflake environment to process documents using AI_PARSE_DOCUMENT and generate text embeddings using EMBED_TEXT_1024 with the voyage-multilingual-2 model. Their Snowflake account is in a region where these specific capabilities or models are only available via cross-region inference. The team needs to ensure these functions work correctly without constant region-specific model selection. Which of the following is the correct configuration action and an important consideration?

Answer: C,E

Explanation:


NEW QUESTION # 133
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