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Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A compliance officer is reviewing the usage of Snowflake Cortex LLM functions and the Cortex REST API within their organization, specifically focusing on the implementation and impact of Cortex Guard. They observe several instances where 'guardrails' were enabled. Which of the following statements accurately describe the behavior and cost considerations of Cortex Guard when integrated with Snowflake Cortex LLM functions or the Cortex REST API?

A) The underlying model for Cortex Guard is Meta's Llama Guard 3, and its processing costs are separate from the primary LLM inference costs.
B) Cortex Guard can be configured with a custom message using the argument in the options object for both 'COMPLETE SQL function and the Cortex REST API.
C) When Cortex Guard is enabled and a response is deemed unsafe, the LLM-generated output is replaced with a predefined message, and only the input tokens for Cortex Guard processing ('guard_tokens') are billed, not the potentially unsafe completion tokens.
D) Cortex Guard is inherently part of all Cortex LLM functions and does not require explicit enablement via 'guardrails: TRUE for SQL functions or the REST API.
E) Cortex Guard operates by evaluating responses after the LLM has fully generated its content, and it incurs additional compute cost for both input and output tokens during its processing.

2. A company is building an enterprise search solution in Snowflake, where user queries are converted into embeddings and then used to find relevant documents from a large corpus. The search logic heavily relies on VECTOR_COSINE_SIMILARITY Which of the following design choices or operational considerations are critical for a robust and efficient implementation using Snowflake's vector capabilities? (Select all that apply)

A) Bind variables can be used to pass query vector literals securely and efficiently to
B) Storing document embeddings in a
C) To keep document embeddings updated efficiently, a
D) For improved retrieval quality in RAG scenarios, it is recommended to split text into smaller chunks, ideally no more than 512 tokens, before generating embeddings for subsequent
E) When deploying custom embedding models or complex search logic, Snowpark Container Services can host GPU-accelerated environments, while

3. An organization has implemented a strict governance policy where the 'ACCOUNTADMIN' has set the 'CORTEX MODELS ALLOWLIST' to only permit 'gemma-7b' and 'llama3.1-8b' models. A developer then executes the following SQL statements in a Snowflake worksheet using 'TRY COMPLETE (SNOWFLAKE.CORTEX)". Assuming no specific RBAC model object grants are in place for the developer's role, what would be the outcome of these queries? SELECT

A) Only the first query will return a completion, as 'gemma-7W is the smallest and most readily available model, while the others will return 'NULL'.
B) The first and third queries will return completions, but the second query will raise an error indicating an unauthorized model attempt.
C) The first and second queries will return completions, while the third will return 'NULL' due to potential resource constraints for larger models.
D) All three queries will return because 'TRY COMPLETE' will always prioritize strict adherence to the allowlist and any model not explicitly listed is considered unavailable.
E) The first query will return a completion, the second will return 'NULL' , and the third will return a completion.

4. 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) Cortex Search automatically handles text chunking and embedding generation for the source data, eliminating the need for manual ETL processes for these steps.
B) 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

C) 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.
D) The
E) 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.

5. 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) Reconfigure the external stage to use
B) Grant the

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) 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.
E) Increase the virtual warehouse size to a Large or X-Large to speed up processing and prevent URL expiration.

Solutions: