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Snowflake GES-C01 Exam Syllabus Topics:

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A Gen AI developer is implementing a Cortex Search Service for a RAG application and needs to configure the text splitting for optimal performance using SNOWFLAKE.CORTEX.SPLIT_TEXT_RECURSIVE_CHARACTER Which of the following statements represent best practices or outcomes when applying text splitting with this function for Cortex Search in a RAG scenario? (Select all that apply)

A) The function automatically enriches each text chunk with relevant metadata about its original document, such as author and creation date, for enhanced filtering capabilities in Cortex Search.
B) Even when using embedding models with larger context windows (e.g., 8000 tokens), a smaller chunk size is typically preferred for improved retrieval and downstream LLM response quality.
C) Snowflake recommends splitting text into chunks of no more than 512 tokens for best search results in Cortex Search.
D) Smaller chunk sizes generally lead to higher retrieval precision for a given query in a RAG system.
E) Optimal text splitting using this function ensures that the number of input tokens precisely equals the number of output tokens for subsequent LLM calls, thereby minimizing compute costs.

2. A data operations team is attempting to scale up their Document AI automated pipeline. They are using a Snowflake Task to process a large volume of daily scanned invoices and receipts, which are stored in an internal stage 'financial_docs stage'. The current processing involves documents that are frequently around 75 MB each, and often there are batches exceeding 1 ,200 documents in a single day. The pipeline is failing consistently. Which of the following factors could be contributing to the failures in this Document AI automated pipeline? (Select all that apply.)

A) The account role executing the task lacks the 'SNOWFLAKCORTEX USER database role, which is a prerequisite for Document AI functions.
B) The was created without specifying 'ENCRYPTION = (TYPE = 'SNOWFLAKE SSE')'.
C) The number of documents in a single daily batch (exceeding 1 ,200) surpasses Document AI's processing limit per query.
D) The Snowflake Task is configured as a 'SERVERLESS TASK, which is not supported by Document AI.
E) The individual document size (75 MB) exceeds the maximum supported file size for Document AI.

3. A financial institution is building a Document AI model to process loan applications. They need to extract all applicant names (which can be multiple, forming a list), classify the application as 'Approved' or 'Rejected' based on various internal criteria (not explicitly stated in the document), and normalize diverse date formats (e.g., '1st Jan 2023', '01/01/2023') to 'YYYY-MM-DD'. Which of the following approaches represent 'best practices' for defining data values and training the Document AI model for these complex extractions? (Select ALL that apply.)

A) To classify applications as 'Approved' or 'Rejected', define a data value like 'What is the application status?' and ensure the training dataset includes annotated examples for both 'Approved' and 'Rejected' classes.
B) For applicant names, define a single value 'applicant_name' and rely solely on advanced prompt engineering to instruct the model on how to identify and list all names, without providing specific examples through annotations.
C) To normalize diverse date formats to 'YYYY-MM-DD', train the model with sufficient annotations, showing the desired output format for various input date formats, allowing Document AI to learn the normalization internally.
D) When defining data values for combinations of values, arrays, or nonstandard formats, fine-tuning the model with annotations that 'show' the expected result is generally more effective than 'telling' it via complex prompt engineering.
E) For extracting lists of items from tables, use individual data values for each column and then merge these lists in the pipeline, ensuring adequate training with sample data, including NULL values and correct order, for reconstruction.

4. A data scientist is preparing to log a custom PyCaret classification model into the Snowflake Model Registry. The goal is to deploy this model on Snowpark Container Services (SPCS) for scalable inference. The PyCaret model relies on the 'pycaret' and 'scipy' Python libraries, and the data scientist has local 'sample data.csv' for inferring the model's signature. Which statements are crucial for successfully logging this custom model for eventual SPCS deployment?

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

5. A data scientist fine-tuned a mistral-lb model in Snowflake for a specific customer support response generation task, naming it my_custom_responder_model. They now want to make this model available for AI_COMPLETE calls in production, ensuring proper access control and regional availability. Which of the following statements is true regarding the deployment and management of this fine-tuned model in Snowflake?

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

Solutions: