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Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. 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) 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.
B) 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.
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) 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.
E) 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.

2. A data engineering team is implementing a Document AI solution to automate the extraction of vendor invoice details. They have already published a Document AI model build, 'vendor_invoice_model', located in New invoices are uploaded to an internal stage, 'fin db.invoice_processing_schema.raw invoices_stage'. The 'invoice_pipeline role' is responsible for executing the
! PREDICT
method on these staged documents as part of an automated task. Which of the following USAGE privileges are essential for to successfully execute the
! PREDICT
method and process documents from the specified stage, assuming the required SNOWFLAKE.DOCUMENT_INTELLIGENCE_CREATOR role is already granted?

A)

B)

C)

D)

E)

3. A financial institution wants to leverage Snowflake Cortex Agents to build an AI application for complex financial analysis, requiring interaction with both their structured transaction databases and unstructured legal documents, while also ensuring intelligent decision- making throughout the process. Which of the following accurately describe the foundational capabilities of Snowflake Cortex Agents?

A) They incorporate a 'Reflection' component, allowing the agent to evaluate results after each tool use and determine the next logical steps, including iterating or clarifying.
B) Agents are designed to orchestrate tasks by planning execution steps, and utilizing tools such as Cortex Analyst for structured data and Cortex Search for unstructured data.
C) Allows integration of custom logic and external services through user-defined functions (UDFs) and stored procedures as custom tools.
D) Cortex Agents enable direct fine-tuning of base LLMs using private customer data, with the resulting models managed within the Snowflake Model Registry.
E) Cortex Agents primarily focus on providing a low-latency, hybrid (vector and keyword) search engine over text data.

4. A data engineering team is setting up an automated pipeline to extract information from new invoices using Document AI. They've created a database and schema Cinvoice_db.invoice_schema') and a Document AI model build They then created an internal stage for documents. When they attempt to run the method on documents uploaded to 'invoice_stage' , they consistently receive the following error:

Given this error message, which 'corrective SQL command' addresses the most likely misconfiguration of the 'invoice_stage' to allow Document AI processing?

A)

B)

C)

D)

E)

5. A development team is building an AI-powered data pipeline in Snowflake. The pipeline involves extracting text from documents, generating embeddings using

,and then performing similarity searches using

to find related documents. They plan to manage this pipeline using Snowflake tasks and want to integrate with a Python application for some custom processing. Considering this scenario, which of the following statements about implementing this pipeline are true?

A)

B) When using Snowflake tasks to automate the embedding generation and similarity search,
C) Snowflake
D) If the team wants to use the Snowpark Python library to call

E) To generate document embeddings, the

Solutions: