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Snowflake GES-C01 Exam Syllabus Topics:

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A data scientist has successfully deployed a Hugging Face sentence transformer model to Snowpark Container Services (SPCS) for GPU-powered inference, making it accessible via an HTTP endpoint. To ensure secure and proper programmatic access to this service from an external application, which of the following statements correctly describe the authentication and access control considerations for calling this public endpoint?

A) The Python API for calling the service requires the Snowflake session object directly, bypassing HTTP endpoint authentication.
B) The default role for the calling user must have the 'SNOWFLAKCORTEX USER database role granted to access the SPCS service via its public endpoint.
C) The public endpoint of the SPCS service can be accessed directly without any authentication, as it's a public endpoint.
D) Applications must use key pair authentication to generate a JSON Web Token (JWT), exchange it with Snowflake for an OAuth token, and then use this OAuth token to authenticate requests to the public endpoint.
E) The 'Authorization' header with a 'Snowflake Token=""' value is a valid method for authenticating requests to the public endpoint programmatically.

2. A data engineer is setting up an automated pipeline to process incoming documents using Snowflake's AI_PARSE DOCUMENT function. To ensure the pipeline functions without errors, which of the following input requirements or limitations must be strictly adhered to?

A) To optimize performance, queries calling the Cortex AI_PARSE_DOCUMENT function should be executed in a large warehouse (e.g., XL or larger), as bigger warehouses increase processing speed for this function.
B) The maximum file size for any document processed by is AI_PARSE_DOCUMENT 100 MB, and it can handle up to 300 pages per document.
C) All documents must be stored in an internal or external stage, but only internal stages support server-side encryption with the SNOWFLAKE_SSE type.
D) For HTML and TXT files, billing for AI_PARSE_DOCUMENT is based on each chunk of 3,000 characters, with the last chunk also being billed even if shorter.
E) Supported file formats for processing include PDF, PPTX, DOCX, JPEG, JPG, PNG, TIFF, TIF, HTML, and TX T, covering a wide range of common document and image types.

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

4. A Snowflake administrator is tasked with monitoring the efficiency and cost-effectiveness of their Cortex Analyst deployments. They need to identify if certain semantic models are generating a high volume of failed or expensive queries. Which of the following approaches or statements are crucial for effectively monitoring and identifying issues with Cortex Analyst usage and associated costs?

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

5. An AI engineer is building an automated pipeline in Snowflake that processes various types of textual data using Cortex AI functions. To ensure the pipeline's stability and avoid failures due to exceeding LLM context windows, they integrate SNOWFLAKE.CORTEX.COUNT_TOKENS and TRY_COMPLETE
. Consider the following code snippets and statements about context window management in Snowflake Cortex.

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

Solutions: