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Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A security auditor needs to access and analyze logs generated by Snowflake AI Observability for compliance auditing and to track the activity of generative AI applications. They need to understand how to reliably query this data and its temporal characteristics within Snowflake. Which of the following statements accurately describes the access and characteristics of this logged data?

A) Logs are exclusively available for analysis through pre-built dashboards in Snowsight and cannot be accessed via direct SQL queries.
B) Access to these detailed event tables is implicitly granted to roles holding the SNOWFLAKE. CORTEX_USER database role and the AI_OBSERVABILITY_EVENTS_LOOKUP application role.
C) Detailed request and response bodies, along with the generated SQL, are stored and can be directly queried using standard SQL.
D) The logs are automatically purged after 7 days of being recorded, requiring a separate process for long-term data retention.
E) Logged data from AI Observability's event tables becomes visible within a small latency, typically 1-2 minutes, after a request is made.

2. An analytics team is preparing documents for a new Document AI model build to extract information from internal policy reviews. They have a variety of documents that they intend to upload to an internal stage for processing. The document list includes: (1) a 70 MB PDF with 100 pages, (2) a 45 MB DOCX with 150 pages, (3) a 30 MB PNG image, (4) a 60 MB TIFF image, and (5) a 20 MB HTML file. All documents are in English. Which of these documents would 'fail' to meet the direct input requirements for Document AI processing?

A) The 70 MB PDF with 100 pages.
B) The 20 MB HTML file.
C) The 60 MB TIFF image.
D) The 30 MB PNG image.
E) The 45 MB DOCX with 150 pages.

3. A Snowflake administrator is tasked with monitoring and optimizing costs for various Gen AI applications leveraging Snowflake Cortex LLM functions. They need to generate a report detailing token consumption for individual API calls to identify high-usage patterns and specific models. Which of the following Snowflake account usage views or methods would provide the most granular insights into prompt, completion, and guardrail token usage for Cortex LLM function calls?

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

4. A data engineering team is implementing a solution using Snowflake Cortex's AI_COMPLETE function to process customer support tickets. They are concerned about sensitive information and ensuring the model's responses are safe, while adhering to Snowflake's data governance principles. Which of the following statements correctly describe the functionality of Cortex Guard and Snowflake's data privacy commitments in this context?

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

5. 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) For performance and cost optimization, it is always recommended to query Cortex Search and the LLM function within a single
C) Using the
D) The

E) The retrieved context from Cortex Search should be directly concatenated with the user's prompt as input to the

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