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Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A developer is refining a Document AI extraction process using the '!PREDICT' method and is meticulously examining the JSON output for invoices, which include 'invoice number', 'invoice items', 'tax amount', and 'vendor name'. They also have a detailed internal table of 'product details' to be extracted. To ensure optimal data quality and accurate interpretation of the extracted information, which of the following best practices or characteristics of Document AI's output should the developer consider?

A) If the 'vendor_name' field cannot be confidently identified in a document, the model will include '"vendor_name": [ { "score": O.X, "value": "NOT FOUND" } l' in the JSON output.
B) The 'ocrScore' provided in the '_documentMetadata' object for each document indicates the model's confidence in the content of specific extracted values, rather than the overall quality of the optical character recognition process.
C) For table extraction, such as the extracted values for each column (e.g., 'tablel litem', 'tablel Igross) are ordered consistently with the rows of the original table, facilitating direct joining of columns.
D) When extracting lists of values, such as 'invoice_items', the Document AI model returns them as an array in the JSON output, preserving the original order of items as they appear in the document.
E) To maximize accuracy when defining data values, questions should be broadly generic (e.g., 'What is the amount?) to allow the Document AI model to infer the most relevant context, especially for fields like 'tax_amount' where multiple numbers might be present.

2. A Gen AI Specialist is responsible for maintaining a Cortex Analyst-powered application. They have defined a semantic model that includes a Verified Query Repository (VQR) to guide user interactions. The application front-end uses the Suggested Questions feature to help users get started. The specialist wants to ensure that a specific set of critical, verified business questions are always displayed to users, regardless of their prior input or the semantic similarity to their current query. Which of the following configuration steps in the semantic model YAML will achieve this requirement?

A)

B)

C)

D)

E)

3. 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) Using the

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

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

4. A data scientist is optimising a Cortex Analyst application to improve the accuracy of literal searches within user queries, especially for high-cardinality dimension values. They decide to integrate Cortex Search for this purpose. Which of the following statements are true about this integration and the underlying data types in Snowflake? (Select all that apply)

A) The cost for embedding data into a Cortex Search Service is primarily incurred per output token generated by the embedding model, as these represent the final vector embeddings, rather than input tokens.
B) Cortex Search Services, when configured as a source for Snowflake dynamic tables, automatically refresh their search index with continuous data updates, maintaining low-latency search results.
C) To integrate Cortex Search with a logical dimension, the semantic model YAML must include a block within the dimension's definition, specifying the service name and optionally a 'literal_column' .
D) The "VECTOR data type in Snowflake, used to store embeddings generated for Cortex Search, is fully supported as a clustering key in standard tables and as a primary key in hybrid tables to accelerate vector similarity searches.
E) For optimal RAG retrieval performance with Cortex Search, it is generally recommended to split text into chunks of no more than 512 tokens, even when using embedding models with larger context windows such as 'snowflake-arctic-embed-l-v2.0-8k'.

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

Solutions: