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Snowflake GES-C01 Exam Syllabus Topics:

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A data operations team is attempting to scale up their Document AI automated pipeline. They are using a Snowflake Task to process a large volume of daily scanned invoices and receipts, which are stored in an internal stage 'financial_docs stage'. The current processing involves documents that are frequently around 75 MB each, and often there are batches exceeding 1 ,200 documents in a single day. The pipeline is failing consistently. Which of the following factors could be contributing to the failures in this Document AI automated pipeline? (Select all that apply.)

A) The individual document size (75 MB) exceeds the maximum supported file size for Document AI.
B) The Snowflake Task is configured as a 'SERVERLESS TASK, which is not supported by Document AI.
C) The was created without specifying 'ENCRYPTION = (TYPE = 'SNOWFLAKE SSE')'.
D) The number of documents in a single daily batch (exceeding 1 ,200) surpasses Document AI's processing limit per query.
E) The account role executing the task lacks the 'SNOWFLAKCORTEX USER database role, which is a prerequisite for Document AI functions.

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 company is developing a RAG application to provide concise and highly relevant answers to user queries from a vast knowledge base of technical documents. They are using Cortex Search for retrieval and are considering different embedding models and text chunking strategies to optimise the system. Which of the following statements about Cortex Search embedding models and RAG best practices are correct? (Select all that apply)

A) The 'voyage-multilingual-2 model is suitable for multilingual documents and has a significantly larger context window (32000 tokens) compared to 'snowflake- arctic-embed-l-v2.C (512 tokens), making it more robust for longer text inputs.
B) For optimal RAG retrieval quality with Cortex Search, it is recommended to split text into chunks of no more than 512 tokens, even when using models with larger context windows like 'snowflake-arctic-embed-l-v2.0-8k'.
C) The cost for embedding models in Cortex Search, such as 'snowflake-arctic-embed-l-v2.0' and 'e5-base-v2, is incurred based on both input and output tokens.
D) The

E) Using the 'snowflake-arctic-embed-l-v2.0-8k' model, which has an 8192-token context window, allows processing entire large technical documents as a single chunk for embedding, leading to better RAG results by preserving full document context.

4. An organization is implementing a two-tier LLM access control strategy in Snowflake. First, common models like 'mistral-7b' and 'llama3.1-8b' need to be broadly accessible to all users granted the 'SNOWFLAKE-CORTEX USER database role. Second, a specialized data science team, using the ANALYST ROLE', requires exclusive access to the higher-capability 'claude-3-5- sonnet' model, which should NOT be generally available through the broad access mechanism. Which set of SQL commands, executed by the 'ACCOUNTADMIN" role, correctly establishes this access control strategy?

A)

B)

C)

D)

E)

5. A financial institution uses Snowflake to process an average of 5 million PDF documents (average 10 pages each) monthly for regulatory compliance using AI_PARSE_DOCUMENT in LAYOUT mode. They are monitoring costs and considering performance optimizations. Given this scenario, which of the following statements about cost and performance for AI_PARSE_DOCUMENT are true?

A) If a document is a HTML file, the billing is based on processing each chunk of 3,000 characters as a page.
B) For optimal results with multilingual documents, the LAYOUT mode supports a broader range of languages than OCR mode.
C) The primary billing metric for in LAYOUT mode is the number of tokens processed, which includes both input and output tokens.
D) Selecting a 'LARGE' or 'X-LARGE' standard warehouse will significantly improve the processing speed and reduce the per-page cost for AI_PARSE_DOCUMENT.
E) To reduce costs, the team should aim to process documents in batches, ensuring each query processes a maximum of 1 ,000 documents to avoid errors.

Solutions: