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Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. 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) For performance and cost optimization, it is always recommended to query Cortex Search and the LLM function within a single
C) To maintain conversational context in a multi-turn chat, the developer should pass all previous user prompts and model responses in the

D) The

E) The retrieved context from Cortex Search should be directly concatenated with the user's prompt as input to the

2. A Gen AI specialist is preparing to upload a large volume of diverse documents to an internal stage for Document AI processing. The objective is to extract detailed information, including lists of items and potentially classifying document types, and then automate this process. Which of the following statements represent 'best practices or important considerations/limitations' when preparing documents and setting up the Document AI workflow in Snowflake? (Select ALL that apply.)

A) When defining data values for extraction, especially for nonstandard formats or combinations of values, fine-tuning the model with annotations is generally more effective than relying solely on complex prompt engineering.
B) If the Document AI model does not find an answer for a specific field, the '!PREDICT method will omit the 'value' key but will still return a 'score' key to indicate confidence that the answer is not present.
C) Documents with a page count exceeding 125 pages or a file size greater than 50 MB will be processed, but with a potential reduction in extraction accuracy.
D) To improve model training, documents uploaded should represent a real use case, and the dataset should consist of diverse documents in terms of both layout and data.
E) For continuous processing of new documents, it is best practice to create a stream on the internal stage and a task to automate the '!PREDICT method execution.

3. A financial analyst is concerned about the rising costs of their Document AI pipeline, which uses 'invoice_model!PREDlCT' to extract data from daily financial reports. They observe that their assigned 'LARGE virtual warehouse is running continuously, even during periods of low document ingestion, contributing significantly to their bill. They want to investigate how to reduce costs effectively for their existing Document AI setup.

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

4. A Snowflake account is located in the AWS US East 1 (N. Virginia) region. The 'ACCOUNTADMIN' has set the 'CORTEX MODELS ALLOWLIST' to "mistral-7b" and 'CORTEX ENABLED CROSS REGION' to 'ANY REGION'. A data scientist, whose role has only the 'SNOWFLAKE.CORTEX USER database role, performs several 'AI COMPLETE calls. Which of the following statements correctly describe the behavior of these calls under the given configuration?

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

5. A team is building a critical Document AI pipeline for continuous processing of new financial reports. They've identified that occasionally, the 'GET_PRESIGNED_URL' generated for specific documents expires before the '!PREDICT function can successfully process them, resulting in HTTP 403 errors. To mitigate this, they plan to modify their SQL query logic.
Which approach effectively addresses the presigned URL expiration issue without altering the stage definition or the model build itself, and adheres to recommended practices for handling batch processing as described in Snowflake's troubleshooting documentation?

A)

B)

C)

D)

Solutions: