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Snowflake GES-C01 Exam Syllabus Topics:

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A Gen AI specialist is preparing to upload a large volume of diverse documents to an internal stage for Document AI processing. The objective is to extract detailed information, including lists of items and potentially classifying document types, and then automate this process. Which of the following statements represent 'best practices or important considerations/limitations' when preparing documents and setting up the Document AI workflow in Snowflake? (Select ALL that apply.)

A) For continuous processing of new documents, it is best practice to create a stream on the internal stage and a task to automate the '!PREDICT method execution.
B) Documents with a page count exceeding 125 pages or a file size greater than 50 MB will be processed, but with a potential reduction in extraction accuracy.
C) When defining data values for extraction, especially for nonstandard formats or combinations of values, fine-tuning the model with annotations is generally more effective than relying solely on complex prompt engineering.
D) To improve model training, documents uploaded should represent a real use case, and the dataset should consist of diverse documents in terms of both layout and data.
E) If the Document AI model does not find an answer for a specific field, the '!PREDICT method will omit the 'value' key but will still return a 'score' key to indicate confidence that the answer is not present.

2. A Gen AI Specialist is leveraging Snowflake Document AI to extract specific entities and table data from a large and varied collection of documents. They are aware of potential limitations and want to understand the expected outcomes when processing different types of files. Considering a scenario where a Document AI model build is used with the '!PREDICT' method, which of the following statements accurately describe the expected behavior or potential issues based on Document AI's conditions and limitations?

A) In a table extraction task, if a specific cell (e.g., 'tablellitem') is empty, the resulting JSON will omit the 'value' key for that cell, but will still provide a 'score' indicating the model's confidence that the cell is empty.
B) If a question for an entity, like 'total_invoice_amount', does not find a corresponding value in a document, the JSON output for will contain a 'value' key with a 'null' string and a 'score' key indicating the model's confidence in the absence of the answer.
C) A document written entirely in Ukrainian will be processed by Document AI, and the extracted information will be of satisfactory quality due to extensive multilingual support.
D) Processing a legal contract document that is 130 pages long will likely result in a '_processingErrors' message indicating that the document has too many pages.
E) If the extracted answer to a question for a single entity (e.g., is very long, it will be automatically truncated to a maximum of 2048 tokens.

3. 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 A
C) Option B
D) Option D
E) Option C

4. A data scientist has developed a Hugging Face sentence transformer model for semantic search and needs to deploy it for GPU- powered inference using Snowpark Container Services (SPCS) in Snowflake. They've already trained the SentenceTransformer model locally. Which of the following statements correctly describe essential considerations for logging and deploying this model, ensuring it leverages GPU resources and appropriate dependencies?

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

5. A multinational corporation is implementing Document AI to automate the processing of purchase orders from various global suppliers. These purchase orders vary significantly in layout and are often submitted in English, German, and Spanish. The data engineering team aims to optimize the preparation phase for effective model training and deployment. Considering Document AI's 'Question optimization best practices' and general document preparation guidelines, which of the following is a 'critical consideration' for successful implementation?

A) For maximum efficiency in defining data values for complex extractions (e.g., lists of line items), prioritize spending extensive time on crafting highly precise and detailed natural language prompts to guide the model.
B) Document AI model builds can only support a single document layout type; therefore, separate model builds must be created for each distinct purchase order layout from different suppliers.
C) For multilingual support, it is mandatory to externally translate all non-English purchase orders to English before uploading them to an internal stage, as
D) The training dataset should be highly diverse, representing various layouts, data variations (including potential NULLs), and containing documents in all target languages (English, German, Spanish) to ensure robust model performance.
E) To simplify prompt engineering, define generic questions such as 'What is the total amount?' irrespective of document layout, as Document AI's foundation model is expected to handle most layout variations through its zero-shot capabilities.

Solutions: