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Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A data application developer is tasked with building a multi-turn conversational AI application using Streamlit in Snowflake (SiS) that leverages the COMPLETE (SNOWFLAKE. CORTEX) LLM function. To ensure the conversation flows naturally and the LLM maintains context from previous interactions, which of the following is the most appropriate method for handling and passing the conversation history?

A) Option D
B) Option E
C) Option B
D) Option A
E) Option C

2. An organization is building a new knowledge base system within Snowflake, which relies on 'SNOWFLAKE.CORTEX.EMBED_TEXT_1024' to generate and store embeddings for documents in a 'VECTOR(FLOAT, 1024)' column. They plan to use these embeddings for semantic search and integrate them into various data processing workflows. Which of the following statements accurately describe limitations or specific compatibility aspects of 'EMBED TEXT 1024' or the 'VECTOR' data type within Snowflake?

A) The 'EMBED function in the Cortex REST API can be used to process a list of text strings, where each individual string can be up to 4096 characters long.
B) When 'EMBED_TEXT 1024' is invoked within a Snowflake dynamic table's SELECT statement, it allows for continuous, automated updates of embeddings as new data arrives.
C) The 'VECTOR data type is not supported in 'VARIANT columns, which means direct storage of embeddings alongside other semi-structured metadata in a single "VARIANT column is not possible.
D) To compare the generated 1024-dimension vectors for similarity, only the 'VECTOR COSINE SIMILARITY function is officially supported by Snowpark Python.
E) The 'VECTOR data type, used to store the output of is fully supported as primary or secondary index keys in Snowflake's hybrid tables.

3.

A)

B)

C)

D)

E)

4. A team is building a critical Document AI pipeline for continuous processing of new financial reports. They've identified that occasionally, the 'GET_PRESIGNED_URL' generated for specific documents expires before the '!PREDICT function can successfully process them, resulting in HTTP 403 errors. To mitigate this, they plan to modify their SQL query logic.
Which approach effectively addresses the presigned URL expiration issue without altering the stage definition or the model build itself, and adheres to recommended practices for handling batch processing as described in Snowflake's troubleshooting documentation?

A)

B)

C)

D)

5. A development team is building a conversational application with Snowflake Cortex Analyst to allow business users to ask follow-up questions about structured dat a. They are specifically designing the multi-turn conversation support and considering the underlying LLM choices for components like the summarization agent. Which of the following statements accurately reflects how Cortex Analyst handles conversational context and best practices for selecting an LLM for its summarization agent?

A) Multi-turn conversation support primarily relies on caching previous SQL query results and re-executing them for follow-up questions, avoiding additional LLM calls for context summarization.
B) Cortex Analyst directly passes the entire raw conversation history to every LLM call for all agents to ensure full context, which generally improves performance.
C) A dedicated LLM summarization agent is introduced before the original workflow to distill conversation history into a concise context for subsequent agents, with Llama 3.1 70B identified as a suitable model due to its high summarization quality.
D) The summarization agent in Cortex Analyst is primarily responsible for generating SQL queries from conversation history, thus requiring an LLM optimized for text- to-SQL tasks.
E) To optimize for latency, Cortex Analyst recommends always using the smallest possible LLM for the summarization agent, such as Llama 3.1 8B, even if it has a slightly higher error rate in rewriting questions.

Solutions: