Snowflake SnowPro® Specialty: Gen AI Certification : GES-C01

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Snowflake GES-C01 Exam Syllabus Topics:

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A business is implementing Snowflake Document AI to process a high volume of scanned legal contracts. The contracts are in PDF format, average 75 pages each, and some are up to 45 MB in size. They need to extract specific clauses, contract dates, and signatory names, including any handwritten elements. The business requires the extracted data to be available for continuous processing within an automated pipeline. Which of the following considerations are critical for successful implementation of this Document AI solution?

A) The primary method for extracting information is This method is exclusively designed for zero-shot extraction, thus fine-tuning is not an option to improve results on specific document types like these contracts.
B) Document AI automatically detects page orientation and supports PDF as a file format. The maximum page limit of 125 pages per document and size limit of 50 MB are well within the specifications for these contracts.
C) For continuous processing of new documents, the business needs to create a stream on a stage and a task to automate the execution of the extracting query. Document AI supports processing up to 500 documents in a single query.
D) When extracting lists of values, such as multiple contract clauses, the Document AI model may initially struggle with complex extractions. It's best practice to train the model with annotations and corrections over a representative dataset to ensure accuracy and correct ordering.
E) To train the Document AI model to extract handwritten text and signatures accurately, particularly if readability is low, the business should utilize the 'ocrScore' from the prediction results to flag documents for image quality review or enhancement.

2. A development team is creating a new search application using Snowflake Cortex Search. They are currently using a 'snowflake-arctic- embed-I-v2.0' embedding model. After an initial load of 10 million rows, each with approximately 500 tokens of text, they observe a significant 'EMBED_TEXT_TOKENS' cost. They want to minimize these costs for future updates and ongoing operations. Considering their goal to optimize 'EMBED_TEXT_TOKENS' costs, which two strategies should the team prioritize for their Cortex Search Service?

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

3. An administrator has configured the 'CORTEX MODELS ALLOWLIST parameter to only permit the 'mistral-large? model at the account level. A user with the role, which has been granted 'SNOWFLAKE.CORTEX USER and 'SNOWFLAKE."CORTEX- MODEL-ROLE-LLAMA3.1-70B"', attempts to execute several queries. Which of the following queries will successfully execute?

A)

B)

C)

D)

E)

4. A Streamlit application developer wants to use AI_COMPLETE (the latest version of COMPLETE (SNOWFLAKE.CORTEX)) to process customer feedback. The goal is to extract structured information, such as the customer's sentiment, product mentioned, and any specific issues, into a predictable JSON format for immediate database ingestion. Which configuration of the AI_COMPLETE function call is essential for achieving this structured output requirement?

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

5. A data scientist is designing a real-time similarity search feature in Snowflake using product embeddings. They plan to use VECTOR_L2_DISTANCE to find similar products. Which statement correctly identifies a cost or data type characteristic relevant to this implementation?

A) The
B) Storing product embeddings generated by
C) The maximum dimension supported for a
D) The
E) Both the

Solutions: