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Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. 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) Implement a mechanism to process documents in smaller batches or extend the expiration time for the presigned URLs to ensure timely access by Document
C) Increase the virtual warehouse size to a Large or X-Large to speed up processing and prevent URL expiration.
D) Grant the

E) 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.

2. A data engineering team is setting up a Retrieval Augmented Generation (RAG) application using Snowflake Cortex Search to provide contextual answers from customer support transcripts. The transcripts are stored in a Snowflake table named SUPPORT _ TRANSCRIPTS. Which of the following statements are crucial considerations or accurate facts regarding the initial setup and configuration of the Cortex Search Service for this use case?

A) Columns specified in the ATTRIBUTES field during service creation are only used for filtering search results and do not need to be present in the source query.
B) The CREATE CORTEX SEARCH SERVICE command requires that CHANGE_TRACKING = TRUE be enabled on the source table, especially if the role creating the service is not the table owner. This ensures that the service can track updates to the base data.
C) Cortex Search is designed to get users up and running quickly with a hybrid (vector and keyword) search engine on text data, handling embedding, infrastructure maintenance, and search quality parameter tuning automatically.
D) The Cortex Search Service can effectively be used as a RAG engine for LLM chatbots by leveraging semantic search capabilities to provide customized and contextualized responses from the text data.
E) Snowflake recommends using a dedicated virtual warehouse of any size, including X-Large or 2X-Large, for each Cortex Search Service to ensure the fastest possible materialization of search indexes during creation and refresh.

3. A development team is implementing a document retrieval system in Snowflake. They plan to store document embeddings and use VECTOR_L2_DISTANCE to find the most relevant documents for a given query embedding. Considering Snowflake's capabilities, which of the following statements are true regarding the use of vector types and VECTOR_L2_DISTANCE
? (Select all that apply)

A) VECTOR
B) Using the Snowpark Python library, developers can directly invoke

C) Document embeddings, which are typically float arrays, can be stored in a
D) O When defining a table column for 1024-dimensional float embeddings, the SQL type specification
E) To prevent issues with direct vector comparisons, explicitly using

4. A data application developer is building a Streamlit chat application within Snowflake. This application uses a RAG pattern to answer user questions about a knowledge base, leveraging a Cortex Search Service for retrieval and an LLM for generating responses. The developer wants to ensure responses are relevant, concise, and structured. Which of the following practices are crucial when integrating Cortex Search with Snowflake Cortex LLM functions like AI_COMPLETE for this RAG chatbot?

A) To maintain conversational context in a multi-turn chat, the developer should pass all previous user prompts and model responses in the

B) For performance and cost optimization, it is always recommended to query Cortex Search and the LLM function within a single
C) The retrieved context from Cortex Search should be directly concatenated with the user's prompt as input to the
D) Using the

E) The

5. A data analytics team is building a Retrieval Augmented Generation (RAG) application to provide contextual answers from a vast repository of internal documents stored in Snowflake. They are evaluating different strategies for generating and retrieving text embeddings to optimize the overall RAG pipeline's performance and relevance. Which of the following statements accurately describe performance considerations related to embedding generation and retrieval in this RAG context? (Select all that apply)

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

Solutions: