Free Access to Snowflake.GES-C01.v2026-03-27.q130 with Valid Practice Test (Page 18)

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?

A.The
B.To maintain conversational context in a multi-turn chat, the developer should pass all previous user prompts and model responses in the
C.The retrieved context from Cortex Search should be directly concatenated with the user's prompt as input to the
D.Using the
E.For performance and cost optimization, it is always recommended to query Cortex Search and the LLM function within a single

Question 82

A data engineer is constructing a Retrieval Augmented Generation (RAG) pipeline in Snowflake to allow users to query a large corpus of unstructured customer support transcripts using natural language. The goal is to retrieve relevant transcript snippets and then use a Large Language Model (LLM) to generate an answer. Which sequence of steps and Snowflake components would effectively implement this RAG pipeline?

A.Option A
B.Option B
C.Option C
D.Option D
E.Option E

Correct Answer: B,D

Both options B and D describe valid and effective ways to implement a RAG pipeline in Snowflake. * *Option B is correct." Cortex Search is specifically designed to act as a RAG engine for LLM chatbots, enabling high-quality fuzzy search over text data. Creating a 'CORTEX SEARCH SERVICE on the text column (e.g., 'transcript_text') and enabling 'CHANGE_TRACKING' on the source table are fundamental steps for continuous updates and retrieval. The retrieved results then provide the necessary context for an LLM call using 'SNOWFLAKE.CORTEX.COMPLETE (or 'AI_COMPLETE). "Option D is correct." This option outlines a more manual, but equally effective, approach to RAG. It involves generating vector embeddings for text chunks using functions like (or the newer 'AI_EMBED), storing these embeddings in a 'VECTOR data type column, and then performing a similarity search (e.g., with *VECTOR COSINE_SIMILARITY) to retrieve relevant content based on a user's query. The retrieved text then serves as context for the LLM (SNOWFLAKE.CORTEX.COMPLETE or 'AI_COMPLETE) to generate a grounded response. Option A is incorrect. While 'SUMMARIZE can produce summaries, it doesn't provide a mechanism for retrieving *semantically similar* text chunks from a large corpus based on a user query, which is crucial for RAG. Storing data in a 'VARIANT' column is also generally not optimal for direct vector embedding or search service operations as described in RAG contexts. Option C is incorrect. Document AI ( ' !PREDICT) is primarily for extracting structured information (entities, tables) from documents, not for general semantic search or RAG over free-form text with LLMs. 'EXTRACT ANSWER is used to find answers within a given text, but it's not the primary orchestrator for a dynamic RAG pipeline that first needs to *retrieve* relevant documents. Option E is incorrect. Cortex Analyst is designed for text-to-SQL functionality over *structured data* and its semantic models, not directly for RAG over *unstructured text corpora'. While it can integrate with Cortex Search for improving literal values in SQL queries, its core purpose is not to act as a RAG engine for unstructured document chat.

Question 83

A development team is building a RAG application in Snowflake Cortex that needs to extract high-fidelity text and layout from a collection of technical documentation PDFs stored in an internal stage to power semantic search and LLM responses. They want to ensure proper context retrieval for complex user queries. Given this scenario, which of the following actions or statements are crucial for effectively leveraging AI_PARSE_DOCUMENT to optimize the RAG pipeline?

A.Option A
B.Option B
C.Option C
D.Option D
E.Option E

Question 84

A data team has implemented a Snowflake data pipeline using SQL tasks that process customer call transcripts daily. This pipeline relies heavily on SNOWFLAKE. CORTEX. COMPLETE() (or its updated alias) for various text analysis tasks, such as sentiment analysis and summary generation. Over time, they observe that the pipeline occasionally fails due to LLM-related errors, and the compute costs are higher than anticipated. What actions should the team take to improve the robustness and cost-efficiency of this data pipeline? (Select all that apply.)

A.Option A
B.Option B
C.Option C
D.Option D
E.Option E

Question 85

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?

A.The semantic model directly executes SQL queries provided by end-users, bypassing the need for an LLM to generate them.
B.It stores user authentication credentials and data access policies, ensuring that only authorized users can interact with the data.
C.The semantic model provides a mapping between business-friendly terms and the underlying technical database schema, enhancing the LLM's ability to generate accurate SQL from natural language questions.
D.It serves as a cache for frequently requested data, reducing latency for natural language queries by providing pre-computed results.
E.The semantic model acts as a vector store, storing embeddings of all data columns to enable semantic search for natural language queries.

Question 81

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Question 85

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