Online Access Free GES-C01 Exam Questions

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.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.
• B.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.
• C.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.
• D.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.
• E.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.

• A.
• B.
• C.
• D.Cross-region inference is automatically managed by Snowflake for allowed models, implying that a new, larger virtual warehouse is required to handle the cross- region data transfer overhead.
• E.

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 C
• D.Option D
• E.Option B

A data engineering team needs to implement a highly accurate, low-latency solution for classifying specialized technical documents into 50 distinct categories. They are considering fine-tuning a Large Language Model (LLM) within Snowflake Cortex for this task. Which of the following considerations are critical for optimizing the fine-tuned model's performance and minimizing inference latency for production use? (Select all that apply)

• A.Option E
• B.Option A
• C.Option C
• D.Option D
• E.Option B

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 E
• B.Option A
• C.Option C
• D.Option D
• E.Option B