Snowflake SnowPro® Specialty: Gen AI Certification - GES-C01 Exam Questions

QUESTION NO: 1
A data analyst is setting up a new Cortex Analyst-powered conversational app for business users. They want to understand how the "Suggested Questions" feature behaves under different semantic model configurations to ensure an optimal user experience. Which of the following statements accurately describe the behavior of the "Suggested Questions" feature in Cortex Analyst based on the semantic model configuration?

QUESTION NO: 2
A data analyst is working with a table named ARTICLE_CONTENT that contains a column (VARCHAR) storing lengthy English articles. They need to generate a concise summary for each article. The analyst plans to use the SNOWFLAKE. CORTEX. SUMMARIZE function. Which of the following accurately describes the syntax and the expected data type of the result for a single article summary?

QUESTION NO: 3
A data engineering team needs to implement a highly accurate, low-latency solution for classifying specialized technical documents into 50 distinct categories. They are considering fine-tuning a Large Language Model (LLM) within Snowflake Cortex for this task. Which of the following considerations are critical for optimizing the fine-tuned model's performance and minimizing inference latency for production use? (Select all that apply)

QUESTION NO: 4
A Snowflake team observes consistently high token costs from 'SNOWFLAKE.ACCOUNT_USAGE.CORTEX_FUNCTIONS_QUERY_USAGE_HISTORY' for a summarization task using the 'mistral- large2' model. The task involves summarizing legal documents, which often exceed the context window of common LLMs. To optimize these token-based costs, which strategy should the team prioritize?

QUESTION NO: 5
A machine learning engineering team is evaluating two different configurations of a Retrieval Augmented Generation (RAG) application. uses for generation, while uses 'mistral-7b' with a refined prompt for the same task. They aim to compare the and 'groundedness' of the generated responses, as well as the efficiency of context retrieval. Which of the following steps are crucial for setting up AI Observability in Snowflake to facilitate a meaningful side-by-side comparison and assess these specific metrics?

QUESTION NO: 6
A data engineer is building an AI data pipeline to automatically extract specific sentiment categories from customer reviews using 'AI_COMPLETE. They want the output to be a structured JSON object containing 'food_quality', 'food_taste', 'wait_time', and 'food cost' with their respective sentiments (e.g., 'positive', 'negative', 'neutral'). The engineer aims for high accuracy and ensures that all these fields are present in the output. Which of the following statements correctly describe the approach to achieve this?

QUESTION NO: 7
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?

QUESTION NO: 8
An organisation is deploying a Snowflake Cortex Agent to assist business users with data insights. To enable users to interact with this agent via the agent : run API, which of the following database roles or privileges must be granted to their account role?

QUESTION NO: 9
A data scientist has fine-tuned a Hugging Face sentence transformer model for semantic search and intends to deploy it to Snowpark Container Services (SPCS) via the Snowflake Model Registry. The model requires GPU acceleration and specific Python packages ('sentence-transformerS, 'torch', 'transformers'). A GPU compute pool named 'my_gpu_pool' is available. Which of the following code snippets correctly logs the model and deploys it as a service to SPCS, ensuring it utilizes the GPU compute pool and has the necessary Python dependencies for the Hugging Face model and PyTorch?

QUESTION NO: 10
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?

QUESTION NO: 11
A company is building an enterprise search solution in Snowflake, where user queries are converted into embeddings and then used to find relevant documents from a large corpus. The search logic heavily relies on VECTOR_COSINE_SIMILARITY Which of the following design choices or operational considerations are critical for a robust and efficient implementation using Snowflake's vector capabilities? (Select all that apply)