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Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A team is developing a Retrieval Augmented Generation (RAG) pipeline in Snowflake, where document chunks are embedded using Cortex AI functions and then retrieved using VECTOR_COSINE_SIMILARITY They are planning their infrastructure and cost management strategy. Which of the following statements correctly describes the cost or performance characteristics of these operations in Snowflake? (Select all that apply)

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

2. 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) Scale down the virtual warehouse to 'X-SMALL' or 'SMALL' size, as larger warehouses do not increase Document AI query processing speed and incur unnecessary costs.
B) Redefine extraction questions to be more generic and encompassing, reducing the number of distinct questions needed per document.
C) Increase the 'max_tokens' parameter within the '!PREDICT' function options to accommodate longer document responses from the model.
D) Implement a pre-processing step to split documents exceeding 125 pages or 50 MB into smaller, compliant files before loading to the stage.
E) Configure the internal stage used for storing invoices with 'ENCRYPTION = (TYPE = 'SNOWFLAKE SSE')'.

3. A development team is preparing to deploy a new Retrieval-Augmented Generation (RAG) application written in Python. They intend to use Snowflake AI Observability to capture detailed logs and traces for debugging and performance analysis. Which of the following configurations are essential prerequisites for enabling this logging capability effectively?

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

4. A company wants to ingest and process scanned invoices and digitally-born contracts in Snowflake. They need to extract all text, preserving layout for contracts and just the text content for scanned invoices. Which AI_PARSE_DOCUMENT modes would be most appropriate for this scenario, and what is the primary purpose of the function itself?

A) Primary purpose is to extract data and layout. For contracts, use LAYOUT mode; for invoices, use OCR mode.
B) Primary purpose is to summarize text. For contracts, use OCR mode; for invoices, use LAYOUT mode.
C) Primary purpose is to generate new text. For contracts, use OCR mode; for invoices, use LAYOUT mode.
D) Primary purpose is to classify text. For contracts, use LAYOUT mode; for invoices, use OCR mode.
E) Primary purpose is to translate text. Both document types should use LAYOUT mode.

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

Solutions: