Snowflake GES-C01 valid study dumps : SnowPro® Specialty: Gen AI Certification Exam

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Snowflake GES-C01 Exam Syllabus Topics:

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A security-conscious data scientist in an Azure East US 2 (Virginia) account wants to fine-tune a mistral -7b model for a specific text summarization task and then deploy it for real-time inference using the Cortex REST API. The mistral-7b base model is natively available for fine-tuning in Azure East US 2 (Virginia). For subsequent inference using the fine-tuned model, they need to understand the regional and cross-region inference considerations. Which of the following statements are correct?

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

2. A data engineering team is tasked with creating vector embeddings for a collection of diverse, multilingual research papers for a semantic search application. They need to use 'SNOWFLAKE.CORTEX.EMBED TEXT 1024' and are considering two models: 'snowflake-arctic-embed-l-v2.0' and 'voyage-multilingual-2'. They also need to ensure the resulting embeddings are stored correctly and understand potential text truncation. Which of the following statements correctly describes the application of the 'EMBED TEXT 1024' function for these models and the characteristics of the generated embeddings?

A) Both 'snowflake-arctic-embed-l-v2.0' and 'nv-embed-qa-4' are multilingual models with a 1024-dimension output, making them interchangeable for this use case.
B) The query

C) The function call

D) The 'snowflake-arctic-embed-I-v2.0' model has a larger context window than 'voyage-multilingual-2 , making it more suitable for longer research papers without truncation.
E) Using either 'snowflake-arctic-embed-l-v2.0' or 'voyage-multilingual-2' with will result in a 'VECTOR(FLOAT, 1024)' data type, which is compatible with 'VARIANT columns.

3. A development team is constructing a Gen AI application using Snowflake Cortex LLM functions, particularly for conversational and text generation tasks. They are concerned about potential high costs due to token consumption. Which of the following strategies would most effectively help minimize token usage and optimize costs when working with these Cortex LLM functions?

A) To encourage more succinct LLM responses and reduce completion_tokens, configure the temperature option to a higher value (e.g., 0.7) in COMPLETE function calls.
B) When employing AI_COMPLETE for structured output tasks, providing concise and highly descriptive explanations for each field within the JSON schema will reduce the input tokens required for the LLM to understand and adhere to the schema accurately.
C) Utilize the COUNT_TOKENS (SNOWFLAKE .CORTEX) helper function to pre-validate the prompt length against the model's context window, thereby preventing truncation errors and subsequent re-runs.
D) For multi-turn conversational experiences using SNOWFLAKE .CORTEX.COMPLETE, only send the most recent user prompt in each API call, as the model automatically retains previous context.
E) In multi-turn conversations within Cortex Analyst, integrate a dedicated LLM summarization agent to rephrase follow-up questions, which reduces the total conversational history passed as context to the main LLM.

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) 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.
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) 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.
D) 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.
E) 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.

5. A data science team is fine-tuning a Snowflake Document AI model to improve the extraction accuracy of specific fields from a new type of complex legal document. They are consistently observing low confidence scores and inconsistent 'value' keys for extracted entities, even after initial training. Which two of the following best practices should the team follow to most effectively improve the model's extraction accuracy and confidence for this complex document type?

A) Actively involve subject matter experts (SMEs) or document owners throughout the iterative process to help define data values, provide annotations, and evaluate the model's effectiveness.
B) Set the 'temperature' parameter to a higher value (e.g., 0.7) during '!PREDICT calls to encourage more creative and diverse interpretations by the model.
C) Limit the fine-tuning training data exclusively to perfectly formatted and clean documents to ensure the model learns from ideal examples without noise.
D) Prioritize extensive prompt engineering by creating highly detailed and complex questions with intricate logic to guide the LLM's understanding of the extraction task.
E) Ensure the training dataset used for fine-tuning includes diverse documents representing various layouts, data variations, and explicit examples of values or empty cells where appropriate.

Solutions: