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Snowflake GES-C01 Exam Syllabus Topics:

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

Question 1

A new Gen AI team member attempts to use Document AI to process a batch of 1 ,500 scanned image files (JPG) that are 70 MB each, stored in an internal stage that was created without specifying an encryption type. Their '!PREDICT' queries consistently fail with various errors. Which of the following are valid reasons for the '!PREDICT' queries to fail in this scenario?

A. The internal stage was not created with 'ENCRYPTION = (TYPE = 'SNOWFLAKE SSE'V, which is a requirement for Document AI.
B. The team member's role lacks the database role, which is essential for using Document AI functions.
C. Processing 1 ,500 documents in one query exceeds the maximum limit for Document AI.
D. The individual JPG files exceed the maximum supported file size for Document AI.
E. JPG is an unsupported file format for Document AI.

Question 2

A data engineering team aims to automatically classify incoming customer support requests into predefined categories ('Technical Issue', 'Billing Inquiry', 'General Question') as part of their Snowflake data ingestion pipeline. The goal is to achieve high classification accuracy while managing LLM inference costs efficiently. Which of the following strategies, when applied within a Snowflake data pipeline using Streams and Tasks, would best contribute to meeting these objectives?

A. Option C
B. Option E
C. Option D
D. Option B
E. Option A

Question 3

A data analyst is setting up a new Cortex Analyst-powered conversational app for business users. They want to understand how the "Suggested Questions" feature behaves under different semantic model configurations to ensure an optimal user experience. Which of the following statements accurately describe the behavior of the "Suggested Questions" feature in Cortex Analyst based on the semantic model configuration?

A. If the semantic model does not include a Verified Query Repository (VQR), Cortex Analyst will always return a blank list of suggested questions.
B. Setting the

C. If multiple verified queries in a VQR are marked with

D. When a semantic model contains a VQR, Cortex Analyst prioritizes returning up to five suggested questions from the VQR that are semantically similar to the user's input.
E. In the absence of a VQR, Cortex Analyst uses underlying Large Language Models (LLMs) to generate up to three suggested questions, which are guaranteed to be answerable.

Question 4

A data engineering team is building a Retrieval Augmented Generation (RAG) pipeline that heavily relies on 'SNOWFLAKE.CORTEX.EMBED_TEXT 768' to process millions of documents daily. They need to optimize for both cost and retrieval quality. Which of the following statements are true regarding the cost and performance of 'EMBED_TEXT 768' in Snowflake? (Select all that apply)

A. To minimize costs for ' EMBED_TEXT 768 operations, it is recommended to execute queries using a smaller virtual warehouse (no larger than MEDIUM), as larger warehouses do not improve performance for these functions.
B. The 'EMBED_TEXT 768' function is billed based on the number of 'output tokens' generated by the embedding model, as this represents the computational complexity of the vector.
C. The 'snowflake-arctic-embed-m-vl .5 model, used by 'EMBED TEXT 768', has a context window of 512 tokens, and texts exceeding this length are truncated before embedding.
D. For optimal retrieval quality in RAG scenarios, text should be split into chunks of no more than 512 tokens before being passed to 'EMBED TEXT 768', even if the model supports a larger context window.
E. The 'EMBED TEXT 768' function, regardless of the 768-dimension model used, has a fixed cost of 1.50 Credits per one million Tokens processed.

Question 5

A Gen AI Specialist is tasked with enhancing a Cortex Analyst semantic model to improve the accuracy of literal string searches for product names within user queries. The product names are stored in a high-cardinality PRODUCT_NAME column in the underlying PRODUCT table. The current semantic model already defines a dimension for PRODUCT_NAME. Which of the following configurations and considerations are essential for integrating Cortex Search with Cortex Analyst to achieve this goal?

A. Option C
B. Option E
C. Option D
D. Option B
E. Option A

Solutions: