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Snowflake SnowPro® Specialty: Gen AI Certification Exam Sample Questions (Q218-Q223):

NEW QUESTION # 218
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,E

Explanation:


NEW QUESTION # 219
A developer has successfully created a Cortex Search Service named within knowledge_base. Services based on a large repository of technical product manuals. They now need to query this service to find sections discussing 'troubleshooting network issues' specifically for products tagged as 'ModelX', returning only the 3 most relevant results and including the 'manual_title' and 'section_id' in the output. Which of the following SQL commands correctly performs this query?

Answer: C

Explanation:


NEW QUESTION # 220
A data engineering team is setting up a Retrieval Augmented Generation (RAG) application using Snowflake Cortex Search to provide contextual answers from customer support transcripts. The transcripts are stored in a Snowflake table named SUPPORT_TRANSCRIPTS. Which of the following statements are crucial considerations or accurate facts regarding the initial setup and configuration of the Cortex Search Service for this use case?

Answer: A,B

Explanation:
Option A is correct because change tracking is required for the Cortex Search Service to monitor updates to the base table, particularly if the service creator is not the table owner. Option B is incorrect; Snowflake recommends using a dedicated warehouse no larger than MEDIUM for each service, as larger warehouses do not necessarily increase performance for index materialization. Option C is incorrect because columns in the ATTRIBUTES field must be included in the source query. Option D is correct as Cortex Search provides a low-latency, high-quality hybrid (vector and keyword) search engine that automatically manages embedding, infrastructure, and search quality tuning. Option E is incorrect because Cortex Search Services currently do not support replication or cloning.


NEW QUESTION # 221
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,C

Explanation:
Option A is incorrect. The user's query is typically embedded (e.g., using to perform a similarity search against the Cortex Search Service. The 'retrieved documents' (context) are then passed to the 'AI COMPLETE function, not the embedding function itself. Option B is correct because to provide a stateful, conversational experience, all previous user prompts and model responses should be passed in the array to the 'COMPLETE or function. Option C is incorrect. While concatenation is a method, for better accuracy and control, the retrieved context should be integrated into a well-engineered prompt, often using tags or specific instructions, rather than just raw concatenation, to guide the LLM's response. Option D is correct because 'AI_COMPLETE Structured Outputs' allows you to supply a JSON schema that completion responses must follow, reducing the need for post-processing and enabling seamless integration with systems requiring deterministic responses. Option E is incorrect. While keeping processing within Snowflake is good for data governance, complex RAG pipelines often involve multiple distinct steps (query embedding, search, retrieval, LLM completion) that may benefit from a staged approach rather than a single monolithic SQL statement. The optimal approach depends on the specific complexity and performance requirements, and a single 'SELECT for the 'entire' RAG flow might not always be the most efficient or practical solution.


NEW QUESTION # 222
A Snowflake administrator is tasked with monitoring the efficiency and cost-effectiveness of their Cortex Analyst deployments. They need to identify if certain semantic models are generating a high volume of failed or expensive queries. Which of the following approaches or statements are crucial for effectively monitoring and identifying issues with Cortex Analyst usage and associated costs?

Answer: A,B,C

Explanation:
Option A is correct because Cortex Analyst logs requests to an event table, and the function can be used to query these logs, which include generated SQL and errors/warnings, helping identify issues. Option C is correct as the 'METERIN_ DAILY_HISTORY view tracks daily credit usage for services, including 'AI_SERVICES' , which encompasses Cortex Analyst. Option E is correct as there is a reported 1-2 minute lag for Cortex Analyst requests to become visible in the event table logs. Option B is incorrect because 'CORTEX_DOCUMENT_PROCESSING_USAGE_HISTORY specifically displays Document AI processing function activity, not all AI Services or Cortex Analyst. Option D is incorrect because Cortex Analyst costs are based on messages, not tokens, so 'prompt_tokens' and 'completion_tokens' would not be relevant for direct cost assessment in this context.


NEW QUESTION # 223
......

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