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Snowflake GES-C01 Exam Syllabus Topics:

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. 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) 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.
B) 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.
C) 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.
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) For multilingual support, it is mandatory to externally translate all non-English purchase orders to English before uploading them to an internal stage, as

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

3. A data engineer is designing a new feature for a Retrieval Augmented Generation (RAG)-based application in Snowflake. They plan to store document embeddings and perform semantic similarity searches to retrieve relevant context for an LLM. Which of the following statements about using the VECTOR data type and related functions in Snowflake are true? (Select all that apply.)

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

4. 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) 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.
C) 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.
D) 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.
E) 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.

5. A data engineering manager needs to audit Cortex LLM function costs to identify specific SQL queries that are unexpectedly high in token consumption for the 'llama3.1-8b' model. They require granular analysis of prompt, completion, and guardrail token usage for these queries. Which of the following Snowflake methods or views would provide the necessary insights?

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

Solutions: