Snowflake Snowflake Certification GES-C01

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Snowflake GES-C01 Exam Syllabus Topics:

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A software development team is building a conversational AI application within Snowflake, aiming to provide a dynamic and stateful chat experience for users. The application needs to handle follow-up questions while maintaining context, provide responses with a degree of creative variation, and actively filter out any potentially harmful content. The team utilizes the SNOWFLAKE. CORTEX. COMPLETE (or AI_COMPLETE) function.

A)

B)

C)

D)

E)

2. 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) 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.
B) 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.
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) 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.

3. A data engineering team is implementing a Document AI solution to automate the extraction of vendor invoice details. They have already published a Document AI model build, 'vendor_invoice_model', located in New invoices are uploaded to an internal stage, 'fin db.invoice_processing_schema.raw invoices_stage'. The 'invoice_pipeline role' is responsible for executing the
! PREDICT
method on these staged documents as part of an automated task. Which of the following USAGE privileges are essential for to successfully execute the
! PREDICT
method and process documents from the specified stage, assuming the required SNOWFLAKE.DOCUMENT_INTELLIGENCE_CREATOR role is already granted?

A)

B)

C)

D)

E)

4. A machine learning team is leveraging the Snowflake Model Registry to manage diverse models, including a custom Python utility for data preprocessing that they wish to make available as a model method. Which of the following statements accurately describe capabilities or considerations when logging models and their associated artifacts and methods in the Model Registry?

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

5. A company is developing a Streamlit application leveraging Snowflake Cortex Analyst for natural language querying over sales data.
They want to implement a robust multi-turn conversational experience where users can ask follow-up questions. Which of the following statements accurately describe the design and cost implications of supporting multi-turn conversations in Cortex Analyst? (Select all that apply)

A) Cortex Analyst supports multi-turn conversations by simply passing the entire conversation history directly to every LLM call within its agentic workflow, which is the most efficient method for maintaining context.
B) The cost for Cortex Analyst's multi-turn conversational support is primarily based on the number of messages processed, and the number of tokens within each message does not directly affect the per-message cost.
C) Developers can manually implement multi-turn conversations in their applications by using the
D) An internal LLM summarization agent is automatically employed by Cortex Analyst before its original workflow to reframe follow-up questions based on conversation history, optimising LLM processing for each agent.
E) When an LLM judge is used to evaluate the summarization quality for multi-turn conversations, a smaller model like Llama 3.1 8B is generally preferred over Llama 3.1 70B to minimise latency, even if it leads to a slightly higher error rate in rewritten questions.

Solutions: