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Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. 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 30 MB PNG image.
B) The 45 MB DOCX with 150 pages.
C) The 20 MB HTML file.
D) The 70 MB PDF with 100 pages.
E) The 60 MB TIFF image.

2. A global analytics firm is developing a Retrieval Augmented Generation (RAG) system in Snowflake to answer customer queries across a large repository of technical documentation, which includes documents in English, German, and Spanish. They are looking to use a Snowflake Cortex embedding model to convert document chunks into vector embeddings for their Cortex Search Service. Which of the following considerations are critical when selecting an appropriate embedding model to optimize for both query relevance and cost-efficiency for their multilingual RAG application? (Select all that apply)

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

3. A Gen AI developer is using 'SNOWFLAKE.CORTEX.COMPLETE" to generate concise summaries of legal documents. Initially, the LLM sometimes provides overly creative or slightly off-topic responses, indicating potential 'hallucinations' or a lack of focus. To improve the factual accuracy and conciseness of the summaries, which combination of prompt engineering techniques and 'COMPLETE' function options should be prioritized?

A) Rely solely on a comprehensive list of 'stop sequences' to end generation when the summary is complete.
B) Implement 'first principles thinking' in the prompt, clearly outlining logical steps for summarization, and set 'temperature' to 0 for deterministic output.
C) Instruct the model to 'think out loud' with an inner monologue in the prompt, and set 'max_tokens' to a large value to allow full reasoning.
D) Provide a 'task description' focusing on broad themes and set 'top_p' to 0.5 to balance creativity and relevance.
E) Use a system prompt defining a persona like 'creative writer' and set 'temperature' to 0.9 to encourage diverse summaries.

4. 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) The logs are automatically purged after 7 days of being recorded, requiring a separate process for long-term data retention.
D) Logged data from AI Observability's event tables becomes visible within a small latency, typically 1-2 minutes, after a request is made.
E) Detailed request and response bodies, along with the generated SQL, are stored and can be directly queried using standard SQL.

5. An enterprise is deploying a new RAG application using Snowflake Cortex Search on a large dataset of customer support tickets. The operations team is concerned about managing compute costs and ensuring efficient index refreshes for the Cortex Search Service, which needs to be updated hourly. Which of the following considerations and configurations are relevant for optimizing cost and performance of the Cortex Search Service in this scenario?

A) For embedding text, selecting a model like

B) CHANGE_TRACKING
C) The

D) The primary cost driver for Cortex Search is the number of search queries executed against the service, with the volume of indexed data (GBImonth) having a minimal impact on overall billing.
E) For optimal performance and cost efficiency, Snowflake recommends using a dedicated warehouse of size no larger than MEDIUM for each Cortex Search Service.

Solutions: