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

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Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A data application developer is tasked with building a multi-turn conversational AI application using Streamlit in Snowflake (SiS) that leverages the COMPLETE (SNOWFLAKE. CORTEX) LLM function. To ensure the conversation flows naturally and the LLM maintains context from previous interactions, which of the following is the most appropriate method for handling and passing the conversation history?

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

2. A data scientist fine-tuned a mistral-lb model in Snowflake for a specific customer support response generation task, naming it my_custom_responder_model. They now want to make this model available for AI_COMPLETE calls in production, ensuring proper access control and regional availability. Which of the following statements is true regarding the deployment and management of this fine-tuned model in Snowflake?

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

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) To maintain conversational context in a multi-turn chat, the developer should pass all previous user prompts and model responses in the

B) Using the

C) For performance and cost optimization, it is always recommended to query Cortex Search and the LLM function within a single
D) The retrieved context from Cortex Search should be directly concatenated with the user's prompt as input to the
E) The

4. A new Gen AI specialist is setting up Document AI. They have successfully created the necessary database, schema, and a custom role named 'doc_ai specialist_role'. This custom role has been granted the 'SNOWFLAKE.DOCUMENT INTELLIGENCE CREATOR database role. However, when the specialist attempts to create a new Document AI model build in Snowsight, they receive the error: Unable to create a build on the specified database and schem a. Please check the documentation to learn more.
What is the most likely missing privilege for the that is preventing the model build creation?

A)

B)

C) The virtual warehouse assigned to the Document AI environment is not large enough to support model build creation.
D)

E)

5. A marketing analyst wants to quickly gauge the overall sentiment of customer feedback stored in a Snowflake table called CUSTOMER_FEEDBACK, which has a column FEEDBACK_TEXT. They decide to use the SNOWFLAKE .CORTEX.SENTIMENT function to process a review. Consider the following SQL query for a specific review:

Which of the following correctly describes the expected output format and interpretation of the sentiment_score for the given input?

A) The output will be a floating-point number between -1 and 1 (inclusive), where a value of 1 indicates strong positive sentiment and -1 indicates strong negative sentiment.
B) The output will be a string like 'Positive' or 'Negative', and a score close to 1 indicates strong positive sentiment.
C) The output will be a boolean value (TRUE/FALSE) indicating if the sentiment is positive, and 0.5 represents a neutral sentiment.
D) The output will be a JSON object containing a 'label' field, and values around 0 indicate a neutral sentiment.
E) The output will be an integer between 0 and 1 00, where higher values denote more positive sentiment.

Solutions: