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Snowflake GES-C01 Exam Syllabus Topics:

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. 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 D
C) Option E
D) Option C
E) Option B

2. A Gen AI Specialist is developing a conversational analytics application using Cortex Analyst, aiming to provide a seamless multi-turn conversation experience for business users querying structured dat a. The team observes that follow-up questions are sometimes misinterpreted, especially when the conversation history is long. Which of the following statements accurately describe how Cortex Analyst handles multi-turn conversations and key considerations for optimizing this functionality?

A) To address misinterpretation in long conversations, the max_tokens parameter for the Cortex Analyst REST API should be significantly increased to ensure the LLM receives the complete historical context without truncation.
B) Multi-turn conversation in Cortex Analyst is primarily handled by the CORTEX_ANALYST_MODEL AZURE_OPENAI parameter, which, when enabled, allows Azure OpenAl models to manage conversational context more effectively.
C) Cortex Analyst incorporates an additional LLM summarization agent before its original workflow to rewrite current-turn questions based on conversation history, with Llama 3.1 70B being a recommended model for this task due to its performance in evaluating summarization quality.
D) Cortex Analyst simply passes the entire conversation history to all subsequent LLM calls, and optimizing this requires manually truncating the array in messages the REST API request.
E) When a user shifts intent frequently in a multi-turn conversation, Cortex Analyst automatically resets the conversation history to prevent misinterpretations and improve accuracy.

3. 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 A
B) Option D
C) Option E
D) Option C
E) Option B

4. A Data Application Developer is building a Streamlit chat application powered by Snowflake Cortex Analyst. Users frequently ask questions involving specific product names, such as "What was the total sales of 'Luxury Coffee Beans' last quarter?". The semantic model has a product_name dimension with high cardinality. The developer wants to ensure Cortex Analyst accurately identifies these specific product literals in user queries. Given this scenario, which of the following approaches should the developer consider to optimize literal search capabilities and enhance Cortex Analyst responses?

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

5. A data team is designing a new Cortex Analyst application and wants to ensure optimal performance, accuracy, and user experience for text-to-SQL conversions. They are particularly interested in how custom instructions interact with other semantic model features and LLM functionalities. Which of the following statements about using in Cortex Analyst are accurate?

A) Using detailed 'custom_instructions' can help mitigate issues where the LLM might struggle with domain-specific terminology or complex business logic not explicitly defined in column descriptions.
B) The presence of 'custom_instructions' in a semantic model can potentially increase the token count for Cortex Analyst requests, as the instructions are passed as additional context to the LLM.
C) Custom instructions are primarily used to define new logical tables or dimensions within the semantic model, effectively extending the data model at runtime.
D) When both 'custom_instructionS and a highly relevant 'verified_query' exist for a user's question, Cortex Analyst will always prioritize the directives from the 'custom_instructions' over the SQL provided in the 'verified_querv'.
E) The 'custom_instructions' in a semantic model directly influence the underlying Large Language Model (LLM) to generate SQL queries that align with specified business context or formatting preferences.

Solutions: