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Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1.

A) The ACCOUNTADMIN role to ensure unrestricted access to all Snowflake Cortex features.
B) The USAGE privilege on the specific database and schema where the Streamlit application and its underlying data tables are located.
C) The CREATE COMPUTE POOL privilege to provision resources for the Streamlit application.
D)

E)

2. A Snowflake administrator is tasked with monitoring and optimizing costs for various Gen AI applications leveraging Snowflake Cortex LLM functions. They need to generate a report detailing token consumption for individual API calls to identify high-usage patterns and specific models. Which of the following Snowflake account usage views or methods would provide the most granular insights into prompt, completion, and guardrail token usage for Cortex LLM function calls?

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

3. A data scientist is working on a new feature that involves querying a Cortex Search Service and integrating the results into various downstream processes. They are concerned about potential data type compatibility issues and limitations within the Snowflake environment. Which of the following statements accurately describe how to query a Cortex Search Service or the limitations of the 'VECTOR data type and Cortex Search itself? (Select all that apply)

A) The 'VECTOR data type, which stores the output of embedding models like those used by Cortex Search, is explicitly not supported in 'VARIANT columns.
B) To retrieve the support ticket most relevant to a query about 'internet issues', filtered to return results only in the 'North America' region, one can use the 'filter' parameter in the 'SEARCH PREVIEW function's JSON argument like so:

C) Cortex Search Services can be configured as a source for Snowflake dynamic tables, enabling continuous and automated synchronization of the search index with base data.
D) Cortex Search Services can only be queried using the 'SNOWFLAKE.CORTEX.SEARCH_PREVIEW' SQL function and do not offer a programmatic interface for applications.
E) The "VECTOR data type, used to store embeddings generated for Cortex Search, is fully supported as a primary key in Snowflake's hybrid tables to accelerate similarity searches.

4. Considering Snowflake's Gen AI principles for cost governance within Snowflake Cortex, an ML engineer is assessing the expenditure for an LLM fine-tuning job. Which option correctly identifies how compute costs for Cortex Fine-tuning are primarily incurred and how fine-tuned models are treated regarding usage by other customers?

A) Costs are incurred per hour of compute pool usage, similar to virtual warehouses. Fine-tuned models are anonymized and used to train future foundation models for all customers.
B) Fine-tuning costs are a flat monthly fee, irrespective of token usage or model size. Fine-tuned models become part of Snowflake's proprietary models after training.
C) Compute costs for fine-tuning are based on the number of tokens used in training, calculated as 'number of input tokens number of epochs trained'. Fine-tuned models built using a customer's data are available exclusively for that customer's use.
D) Only inference using fine-tuned models incurs costs, not the training itself. Fine-tuned models can be openly shared on the Snowflake Marketplace.
E) Costs are based on the number of fine-tuning jobs created, not tokens. Fine-tuned models are shared across all Snowflake customers to improve the general service.

5. A data pipeline processes thousands of scanned legal contracts daily using a Document AI model via the '!PREDICT function. The process involves fetching presigned URLs for documents from an internal stage using 'GET PRESIGNED URL'. Recently, the pipeline has started failing intermittently, returning the error:

The data engineering team confirms network connectivity and privileges are stable. Which of the following is the most likely cause of this issue?

A) The Document AI model build has not been published to the production environment.
B) The documents contain handwritten annotations, which Document AI struggles to process effectively.
C) The
D) The internal stage is not configured with
E) The virtual warehouse assigned to the Document AI pipeline is undersized (e.g., X-Small), leading to slow processing that indirectly causes URL expiration.

Solutions: