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Snowflake GES-C01 Exam Syllabus Topics:

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

Question 1

A marketing team is analyzing social media comments using Snowflake and wants to categorize them into predefined campaign sentiments (e.g., 'Positive Campaign Engagement', 'Negative Campaign Feedback', 'Neutral Discussion'). They decide to use the SNOWFLAKE. CORTEX. CLASSIFY TEXT function for this task. Which of the following statements about its usage are correct?

A. The input string to CLASSIFY_TEXT is case-insensitive, meaning 'Great product!' and 'great product!' will yield identical classification results due to automatic normalization.
B. CLASSIFY_TEXT can return a JSON object with a 'label' field, where the value of this field indicates the classified category of the input text.
C. To provide more context and potentially improve classification accuracy, categories within the can be defined as SQL objects, including 'description' and 'examples' fields.
D. If the input text exceeds a model-specific token limit, CLASSIFY_TEXT will automatically truncate the text before processing without raising an error.
E. The argument must contain exactly two string values for effective binary classification, otherwise an error is returned.

Question 2

A data engineer is tasked with establishing AI Observability for a generative AI application that integrates with external systems and will undergo continuous improvement. The goal is to compare different iterations of the application efficiently. Which combination of configuration best practices, features, and governance aspects are most relevant for a robust setup of AI Observability within Snowflake for this scenario?

A. Ensure the Python environment includes 'trulens-core', 'trulens-connectors-snowflake', and 'trulens-providers-cortex' (version 2.1.2 or later) and set the environment variable TRULENS_OTEL_TRACING to 1.
B. Run the AI Observability project directly within a Snowflake Notebook to leverage its integrated environment for easier debugging and iteration.
C. To compare different LLMs or prompt configurations, rely on the AI Observability's 'Comparisons' feature, which allows side-by-side analysis of evaluation metrics across multiple evaluations.
D. If the AI Observability service is not natively available in the primary region, enable to ' ANY_REGION' or a specific supported region to allow tracing and evaluation to proceed.
E. For access control, the role used to create and execute runs must be granted the 'SNOWFLAKE.CORTEX_USER database role and the 'AI_OBSERVABILITY_EVENTS_LOOKUPS application role.

Question 3

A data application developer is building a Streamlit chat application within Snowflake. This application uses a RAG pattern to answer user questions about a knowledge base, leveraging a Cortex Search Service for retrieval and an LLM for generating responses. The developer wants to ensure responses are relevant, concise, and structured. Which of the following practices are crucial when integrating Cortex Search with Snowflake Cortex LLM functions like AI_COMPLETE for this RAG chatbot?

A. The retrieved context from Cortex Search should be directly concatenated with the user's prompt as input to the
B. To maintain conversational context in a multi-turn chat, the developer should pass all previous user prompts and model responses in the
C. For performance and cost optimization, it is always recommended to query Cortex Search and the LLM function within a single
D. Using the
E. The

Question 4

A data scientist is implementing a Retrieval Augmented Generation (RAG) system in Snowflake for a legal document repository. They need to convert legal document chunks into vector embeddings and efficiently find the most relevant document chunks based on a user's query. Which of the following statements accurately describe the process and best practices for creating and using these vector embeddings with Snowflake Cortex LLM functions?

A. Option C
B. Option B
C. Option D
D. Option A
E. Option E

Question 5

A business team using a Snowflake Cortex Analyst-powered chatbot reports that follow-up questions in multi-turn conversations are sometimes slow to process, impacting user experience. The development team wants to optimize for responsiveness while maintaining accuracy in SQL generation. Which of the following strategies directly addresses latency in multi-turn conversations within Cortex Analyst, considering its underlying mechanisms?

A. Configure the semantic model to reset the conversation context after every three turns to limit token count.
B. Increase the warehouse size used for Cortex Analyst queries to 'Large' to accelerate LLM inference.
C. Rely on
D. Implement an explicit LLM summarization agent within the semantic model to condense conversation history before it's passed to subsequent LLM calls.
E. Switch the underlying text-to-SQL LLM to a smaller model, such as

Solutions: