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Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. 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) 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.
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) 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.

2. A data scientist is preparing a large text document for processing with Snowflake Cortex LLMs. They need to use the COUNT_TOKENS function to estimate the input token count for different models. Given the statement

which of the following statements about the COUNT_TOKENS function and its output is TRUE?

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

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

4. A data engineering team is building an automated pipeline in Snowflake to process customer reviews. They need to use AI_COMPLETE to extract specific details like product, sentiment, and issue type, and store them in a strictly defined JSON format for seamless downstream integration. They aim to maximize the accuracy of the structured output and manage potential model limitations. Which statements accurately reflect the best practices and characteristics when using AI_COMPLETE with structured outputs for this scenario?

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

5. A security engineer is developing an application that uses the Snowflake Cortex REST API to interact with LLMs, specifically to obtain structured outputs for text classification and to ensure secure communication. They are focusing on the /api/v2/cortex/ inference : complete endpoint. Which of the following statements correctly describe aspects of this interaction?

A) Authentication for Cortex REST API requests is primarily handled through an Authorization: Bearer header, where the token can be a JSON Web Token (JWT), OAuth token, or programmatic access token.
B) To ensure the most consistent and deterministic structured output from the LLM, it is recommended to set the temperature option to a higher value, such as 0.7 or 1.0, in the request payload.
C) For models like OpenAI (GPT) used via the Cortex REST API with structured output, the JSON schema in the response_format field must include "additionalProperties": false and a "required" field listing all properties at every node.
D) To strictly enforce a JSON schema for the LLM's response, the response_format parameter must be included in the request body, supplied as a JSON schema object, which helps reduce post-processing efforts.
E) The Cortex REST API for LLM inference always returns the complete LLM response as a single, fully-formed JSON object once generation is finished, regardless of any streaming options.

Solutions: