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Snowflake GES-C01 Exam Syllabus Topics:

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A data scientist wants to fine-tune a
mistral -7b
model to improve its ability to generate specific product descriptions based on brief input features. They have a table named PRODUCT_CATALOG with columns PRODUCT_FEATURES (text) and GENERATED_DESCRIPTION (text). Which of the following statements correctly describe the preparation and initiation of this fine-tuning job in Snowflake Cortex?
(Select all that apply)

A) The SQL query for the training data must select columns aliased as
B) The
C) O To generate highly structured
D) The fine-tuning job must be created using a
E) Once a fine-tuned model is created, it is fully managed by the Snowflake Model Registry API, allowing for programmatic updates to its parameters and versions.

2. A Gen AI developer is implementing a Document AI solution to extract key fields from thousands of diverse PDF reports, which vary significantly in length and complexity. They use the '!PREDICT method with 'GET_PRESIGNED_URL' to process documents from an external stage. After initial testing, they observe two distinct types of errors in the query results:

for other, lengthy PDF files. Which two of the following actions should the developer take to resolve these issues?

A) Redesign the input documents to ensure they do not exceed 125 pages per file, or preprocess by splitting overly long documents into multiple smaller files.
B) Increase the virtual warehouse size to a Large or X-Large to speed up processing and prevent URL expiration.
C) Implement a mechanism to process documents in smaller batches or extend the expiration time for the presigned URLs to ensure timely access by Document
D) Grant the

E) Reconfigure the external stage to use

3. An organisation is deploying a Snowflake Cortex Agent to assist business users with data insights. To enable users to interact with this agent via the agent : run API, which of the following database roles or privileges must be granted to their account role?

A) The
B) CREATE EXTERNAL AGENT
C) The
D) The
E) The

4. A data engineering team is planning to build a real-time data pipeline using Snowflake's dynamic tables to process incoming log dat a. They want to use SNOWFLAKE. CORTEX. EXTRACT_ANSWER to pull out specific error codes and timestamps from log entries. They are also mindful of the operational costs. Which of the following statements accurately describes limitations or cost considerations for using SNOWFLAKE . CORTEX. EXTRACT_ANSWER in this scenario?

A) If a source_document input exceeds the model's token limit, EXTRACT_ANSWER will automatically truncate the text, potentially impacting results but avoiding an error.
B) The EXTRACT_ANSWER function does not support dynamic tables, which will prevent its direct use in this pipeline design.
C) To optimize performance and reduce cost for large-scale log processing, it is recommended to execute EXTRACT_ANSWER queries on a Snowpark-optimized warehouse of at least 'LARGE' size.
D) EXTRACT_ANSWER is explicitly designed for multi-language extraction, making it suitable for logs that might contain various languages without affecting cost.
E) The billing for EXTRACT_ANSWER is based on the combined token count of both the source_document (log entry) and the question used for extraction.

5. A data science team is planning to implement a new RAG (Retrieval Augmented Generation) application using Snowflake Cortex, specifically leveraging Cortex Search. They are evaluating the key features, best practices, and cost considerations associated with Cortex Search. Which of the following statements accurately describe aspects of Cortex Search?

A) The credit cost for Cortex Search Services is primarily based on the number of queries executed against the service, not the amount of indexed data.
B) Cortex Search automatically handles embedding, infrastructure maintenance, and ongoing index refreshes, and can be used as a backend for enterprise search or a RAG engine for LLM chatbots.
C) For best search results, Snowflake recommends splitting text in the search column into chunks of no more than 512 tokens, even when longer-context embedding models are available.
D) Cortex Search supports only English-only embedding models; multilingual RAG applications require external embedding solutions.
E) Cortex Search Services require a virtual warehouse for initial setup and subsequent refreshes to run queries against base objects and build the search index.

Solutions: