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Snowflake GES-C01 Exam Syllabus Topics:

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A company wants to ingest and process scanned invoices and digitally-born contracts in Snowflake. They need to extract all text, preserving layout for contracts and just the text content for scanned invoices. Which AI_PARSE_DOCUMENT modes would be most appropriate for this scenario, and what is the primary purpose of the function itself?

A) Primary purpose is to classify text. For contracts, use LAYOUT mode; for invoices, use OCR mode.
B) Primary purpose is to extract data and layout. For contracts, use LAYOUT mode; for invoices, use OCR mode.
C) Primary purpose is to generate new text. For contracts, use OCR mode; for invoices, use LAYOUT mode.
D) Primary purpose is to translate text. Both document types should use LAYOUT mode.
E) Primary purpose is to summarize text. For contracts, use OCR mode; for invoices, use LAYOUT mode.

2. An operations team is investigating an issue with a generative AI application powered by Snowflake Cortex Analyst, where users reported unexpected behavior in generated SQL. To diagnose the problem, they examine the detailed event logs captured by Snowflake AI Observability. Which categories of information can they expect to find in these event tables to assist their investigation?

A) Real-time CPU and memory usage statistics for the Snowflake virtual warehouse executing the LLM inference.
B) The full text of the natural language questions submitted by the users.
C) The exact SQL queries that Cortex Analyst generated in response to user questions.
D) Any error messages or warnings that occurred during the processing of the request.
E) The complete request and response bodies associated with the application's execution steps.

3. A developer is refining a Document AI extraction process using the '!PREDICT' method and is meticulously examining the JSON output for invoices, which include 'invoice number', 'invoice items', 'tax amount', and 'vendor name'. They also have a detailed internal table of 'product details' to be extracted. To ensure optimal data quality and accurate interpretation of the extracted information, which of the following best practices or characteristics of Document AI's output should the developer consider?

A) The 'ocrScore' provided in the '_documentMetadata' object for each document indicates the model's confidence in the content of specific extracted values, rather than the overall quality of the optical character recognition process.
B) If the 'vendor_name' field cannot be confidently identified in a document, the model will include '"vendor_name": [ { "score": O.X, "value": "NOT FOUND" } l' in the JSON output.
C) To maximize accuracy when defining data values, questions should be broadly generic (e.g., 'What is the amount?) to allow the Document AI model to infer the most relevant context, especially for fields like 'tax_amount' where multiple numbers might be present.
D) When extracting lists of values, such as 'invoice_items', the Document AI model returns them as an array in the JSON output, preserving the original order of items as they appear in the document.
E) For table extraction, such as the extracted values for each column (e.g., 'tablel litem', 'tablel Igross) are ordered consistently with the rows of the original table, facilitating direct joining of columns.

4. A data scientist needs to generate vector embeddings for product descriptions stored in a column 'PRODUCT_DESCRIPTION' in the 'PRODUCT_CATALOG' table. They want to use the 'e5-base-v2 model for this task. Which of the following SQL statements correctly applies the 'SNOWFLAKE.CORTEX.EMBED TEXT 768' function and accurately describes the expected data type of the resulting embedding?

A) The query

B) The query

C) The query

D) The query

E) The query

5. A data analytics team aims to enhance their understanding of customer feedback stored in a Snowflake table called CUSTOMER_FEEDBACK. This table has a REVIEW_TEXT column containing raw customer comments and a CUSTOMER_SEGMENT column. The team wants to classify each review into predefined categories and then generate a concise summary of all reviews for each customer segment. Which of the following Snowflake Cortex AI functions and approaches should they use?

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

Solutions: