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Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A business user frequently asks Cortex Analyst questions that require filtering on specific product names, such as "What were the sales for 'iced tea' last month?" The 'product' dimension has many distinct values (high cardinality), and Cortex Analyst sometimes struggles to accurately identify the exact literal product name, leading to less precise SQL queries. The Gen AI Specialist wants to enhance Cortex Analyst's ability to find these literal values for the 'product' dimension. To improve Cortex Analyst's literal search capability for the high-cardinality 'product' dimension, which of the following is the most appropriate and recommended approach to configure in the semantic model?

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

2. An enterprise is designing an advanced generative AI application in Snowflake, leveraging Cortex Agents to orchestrate data analysis from both structured and unstructured sources. According to Snowflake's Gen AI principles and the capabilities of Cortex Agents, which of the following statements accurately describe the workflow components and the types of tools an agent can utilize?

A) For debugging, Cortex Agents allow direct modification of the LLM's internal state to refine accuracy, latency, and cost during execution.
B) The agent's workflow includes 'Planning' to orchestrate a solution, 'Explore options' for disambiguation, and 'Reflection' to determine next steps after tool use. Supported tools include Cortex Analyst and Cortex Search.
C) Cortex Agents primarily focus on pre-defined, single-turn SQL queries for structured data, with limited support for unstructured data processing.
D) Cortex Agents can orchestrate across both structured and unstructured data sources, and custom tools can be implemented using Snowflake stored procedures and user-defined functions (UDFs).
E) Cortex Agents are restricted to using only Snowflake's native Cortex LLM functions; custom logic via UDFs or stored procedures is not supported for tool implementation.

3. A data engineering team is setting up an automated pipeline in Snowflake to process call center transcripts. These transcripts, once loaded into a raw table, need to be enriched by extracting specific entities like the customer's name, the primary issue reported, and the proposed resolution. The extracted data must be stored in a structured JSON format in a processed table. The pipeline leverages a SQL task that processes new records from a stream. Which of the following SQL snippets and approaches, utilizing Snowflake Cortex LLM functions, would most effectively extract this information and guarantee a structured JSON output for each transcript?

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

4. 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) 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.
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) 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.
D) 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.
E) 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.

5. A Snowflake administrator is tasked with ensuring that a specific data science team can only use approved LLMs (mistral-7b, llama3.1-8b) for generative AI tasks within a particular schema, and also needs to enable the use of an LLM in a non-native region due to specific project requirements. Which combination of configurations would meet these requirements?

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

Solutions: