Snowflake GES-C01 Exam Questions : SnowPro® Specialty: Gen AI Certification Exam

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Snowflake GES-C01 Exam Syllabus Topics:

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. 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) Increase the 'max_tokenS parameter within the ' !PREDICT' function options to accommodate longer document responses from the model.
B) Implement a pre-processing step to split documents exceeding 125 pages or 50 MB into smaller, compliant files before loading to the stage.
C) Scale down the virtual warehouse to 'X-SMALC or 'SMALL' size, as larger warehouses do not increase Document AI query processing speed and incur unnecessary costs.
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'Y.

2.

A)

B)

C)

D)

E)

3. A data engineering team is preparing a large corpus of unstructured text documents for a Retrieval Augmented Generation (RAG) application in Snowflake, leveraging Cortex Search and LLM functions. They plan to use SNOWFLAKE.CORTEX.SPLIT_TEXT_RECURSIVE_CHARACTER as part of their data ingestion pipeline. What is the primary benefit of employing this helper function in the context of their RAG workflow?

A) It generates vector embeddings for each document chunk, eliminating the need for separate embedding models.
B) It compresses the text data to reduce storage costs in Snowflake stages before processing by embedding models.
C) It automatically translates documents into a target language, ensuring multilingual compatibility for the LLM.
D) It divides lengthy documents into smaller, manageable text chunks, which improves the precision of information retrieval and the relevance of downstream LLM responses.
E) It performs sentiment analysis on each chunk, allowing the RAG system to filter out negative or irrelevant content before retrieval.

4. A business team using a Snowflake Cortex Analyst-powered chatbot reports that follow-up questions in multi-turn conversations are sometimes slow to process, impacting user experience. The development team wants to optimize for responsiveness while maintaining accuracy in SQL generation. Which of the following strategies directly addresses latency in multi-turn conversations within Cortex Analyst, considering its underlying mechanisms?

A) Switch the underlying text-to-SQL LLM to a smaller model, such as
B) Increase the warehouse size used for Cortex Analyst queries to 'Large' to accelerate LLM inference.
C) Implement an explicit LLM summarization agent within the semantic model to condense conversation history before it's passed to subsequent LLM calls.
D) Rely on
E) Configure the semantic model to reset the conversation context after every three turns to limit token count.

5. A data engineering team is setting up a pipeline to automatically process various document types using AI_PARSE_DOCUMENT from an internal stage. Before writing any SQL, they need to ensure their Snowflake environment and the role they will use have the necessary permissions and configurations. Which of the following statements correctly describe essential prerequisites or access control requirements for successfully using AI_PARSE_DOCUMENT in this setup?

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

Solutions: