Snowflake Certification GES-C01

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Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A data scientist needs to generate vector embeddings for product descriptions stored in a column 'PRODUCT_DESCRIPTION' in the 'PRODUCT_CATALOG' table. They want to use the 'e5-base-v2 model for this task. Which of the following SQL statements correctly applies the 'SNOWFLAKE.CORTEX.EMBED TEXT 768' function and accurately describes the expected data type of the resulting embedding?

A) The query

B) The query

C) The query

D) The query

E) The query

2. A data platform architect is evaluating the integration of 'SNOWFLAKE.CORTEX.TRANSLATE into several automated data pipelines. One pipeline involves real-time translation of messages for a chat application, while another is for batch processing of archived documents. The architect is considering various Snowflake features for orchestration and deployment. Which of the following considerations about 'SNOWFLAKE.CORTEX.TRANSLATE is accurate?

A) If 'TRANSLAT' is not natively available in the account's primary Snowflake region, cross-region inference cannot be enabled, thus preventing its use.
B) The Snowflake managed model used by the 'TRANSLATE' function has a context window of 4,096 tokens, meaning texts longer than this will be truncated before translation.
C) When using 'TRANSLATE within a Snowpark Python User-Defined Function (UDF), the raw text data must be explicitly moved out of Snowflake's network boundary to the underlying LLM service for translation.
D) To manage potential failures in a production pipeline, 'SNOWFLAKCORTEX.TRANSLATE should be wrapped in 'TRY COMPLETE' for robust error handling, returning NULL on failure instead of an error.
E) The 'TRANSLATE' function can be seamlessly integrated into a dynamic table's ' SELECT statement to provide continuous, automated translation with minimal configuration.

3. An ML engineer is designing a Cortex Agent to provide highly accurate and contextualized responses. They intend for the agent to use state-of-the-art LLMs for orchestration and to maintain a specific brand tone in its outputs. Considering the available models and configurations for Cortex Agents, which statement is true?

A) When an agent utilizes an LLM for orchestration, the system ensures that cross-region inference is automatically enabled without any latency implications, making region selection irrelevant.
B) To ensure the agent's responses adhere to a desired brand and tone, 'Response instructions' can be configured, which guide the agent's output style and persona.
C) Cortex Agents are restricted to using only Snowflake Arctic models for orchestration, due to security and governance requirements, and must operate in the account's default region.
D) The agent's 'Planning' component is specifically responsible for evaluating results after each tool use and deciding the subsequent steps in the query resolution process, acting as a feedback loop.
E) Cortex Agents primarily interact with data through the Snowflake Model Registry API to retrieve and update fine-tuned model parameters during their iterative planning phase.

4. A data engineering team is implementing a Document AI solution to automate the extraction of vendor invoice details. They have already published a Document AI model build, 'vendor_invoice_model', located in New invoices are uploaded to an internal stage, 'fin db.invoice_processing_schema.raw invoices_stage'. The 'invoice_pipeline role' is responsible for executing the
! PREDICT
method on these staged documents as part of an automated task. Which of the following USAGE privileges are essential for to successfully execute the
! PREDICT
method and process documents from the specified stage, assuming the required SNOWFLAKE.DOCUMENT_INTELLIGENCE_CREATOR role is already granted?

A)

B)

C)

D)

E)

5. A data scientist is tasked with improving the accuracy of an LLM-powered chatbot that answers user questions based on internal company documents stored in Snowflake. They decide to implement a Retrieval Augmented Generation (RAG) architecture using Snowflake Cortex Search. Which of the following statements correctly describe the features and considerations when leveraging Snowflake Cortex Search for this RAG application?

A) The
B) Enabling change tracking on the source table for the Cortex Search Service is optional; the service will still refresh automatically even if change tracking is disabled.
C) For optimal search results with Cortex Search, source text should be pre-split into chunks of no more than 512 tokens, even when using models with larger context windows like

D) To create a Cortex Search Service, one must explicitly specify an embedding model and manually manage its underlying infrastructure, similar to deploying a custom model via Snowpark Container Services.
E) Cortex Search automatically handles text chunking and embedding generation for the source data, eliminating the need for manual ETL processes for these steps.

Solutions: