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Snowflake GES-C01 Exam Syllabus Topics:

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A data engineering team is setting up a new Cortex Search Service named to power a RAG application over their table, which stores historical ticket text and metadata. They need to ensure proper setup, cost efficiency, and data integrity. Which of the following statements are true regarding the creation and initial configuration of this Cortex Search Service? (Select all that apply)

A) Columns intended to be filterable in search queries must be explicitly listed in the 'ATTRIBUTES' field during service creation and must also be included in the source query for the service.
B) The role used to create the Cortex Search Service must be granted the 'SNOWFLAKE.CORTEX_USER database role.
C) To enable continuous updates of the search index as new tickets are added,

D) The 'CREATE CORTEX SEARCH SERVICE command should specify a Snowpark-optimized warehouse for optimal performance, as it is designed for memory-intensive ML workloads.
E) If the service is created using the Snowsight AI & ML Studio, its name will be double-quoted, and thus must be double-quoted when referenced in subsequent SQL queries.

2.
Which 'combination of missing schema-level privileges' is explicitly cited in the documentation as a direct cause for this error, assuming a unique model build name?

A)

B)

C)

D)

E)

3. A Snowflake administrator needs to implement a granular access control strategy for LLMs. The general policy is to restrict access to a select few models via an account-level allowlist. However, a specific data science team (using role 'DATA SCIENCE TEAM ROLE) requires access to the 'claude-3-5-sonnet' model, which should not be available to other users or globally via the allowlist. Given this scenario, which set of commands would correctly establish this access control while adhering to the specified requirements?

A)

B)

C)

D)

E)

4. A data engineering team is designing a pipeline in Snowflake to translate a continuous stream of multi-language customer support tickets into English using 'SNOWFLAKE.CORTEX.TRANSLATE. They are concerned about potential language identification issues and the overall cost implications. Which of the following statements are true regarding the use of 'SNOWFLAKE.CORTEX.TRANSLATE for this scenario? (Select all that apply)

A) For cost efficiency, Snowflake recommends using a larger warehouse (e.g., XL or 2XL) for executing queries that call 'TRANSLATE functions, as this significantly reduces the per-token processing cost.
B) The 'TRANSLATE' function is exclusively billed based on the number of input tokens, as it primarily analyzes existing text rather than generating new content.
C) If the source language of a ticket is unknown or contains mixed languages (e.g., 'Spanglish'), the function can still process it by specifying an empty string ') for the source _ language argument.
D) The fixed billing rate for the 'TRANSLATE function is 1.50 Credits per one million Tokens processed.
E) Snowflake Cortex functions, including 'TRANSLATE, add an internal prompt to the user's input text, which increases the total input token count for billing purposes beyond the raw text length.

5. A data processing team is using Snowflake Document AI to extract data from incoming supplier invoices. They observe that many documents are failing to process, and successful extractions are taking longer than expected, leading to increased costs. Upon investigation, they find error messages such as

. Additionally, their 'X-LARGE virtual warehouse is constantly active, contributing to higher-than-anticipated bills. Which two of the following actions are essential steps to troubleshoot and address the root causes of these processing errors and optimize their Document AI pipeline?

A) Implement a pre-processing step to split documents exceeding 125 pages or 50 MB into smaller, compliant files before loading to the stage.
B) Scale down the virtual warehouse to 'X-SMALL' or 'SMALL' size, as larger warehouses do not increase Document AI query processing speed and incur unnecessary costs.
C) Increase the 'max_tokens' parameter within the '!PREDICT' function options to accommodate longer document responses from the model.
D) Redefine extraction questions to be more generic and encompassing, reducing the number of distinct questions needed per document.
E) Configure the internal stage used for storing invoices with 'ENCRYPTION = (TYPE = 'SNOWFLAKE SSE')'.

Solutions: