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Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A multi-national corporation uses Snowflake across several AWS regions. Their primary operational Snowflake account is in AWS US East (Ohio), but they need to leverage a specific AI_COMPLETE model, llama4-maverick, which is natively available in AWS US East 1 (N. Virginia) but not in US East (Ohio). To address this, the Snowflake administrator enables cross-region inference for their US East (Ohio) account.

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

2. 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) Cortex Search automatically handles text chunking and embedding generation for the source data, eliminating the need for manual ETL processes for these steps.
B) The
C) 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.
D) 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.
E) 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

3. 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) 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.
B) 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.
C) 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.
D) 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.
E) 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.

4. A Gen AI developer has a Document AI pipeline that uses a query with 'GET PRESIGNED URL' to process multi-page PDF documents. Despite the internal stage being correctly set up with 'SNOWFLAKE SSE' encryption and the model build being published, they observe inconsistent results. Some documents result in a Received HTTP 403 response for presigned URL. URL may be expired.
error, while other documents (containing complex diagrams and dense text in an unsupported language like Korean) are processed, but the extracted information is often incomplete or inaccurate.
Which two factors are most likely contributing to these observed issues?

A) The Document AI model is returning answers longer than its limit of 512 tokens for entity extraction or 2048 tokens for table extraction.
B) The '!PREDICT method is being called with an outdated model build version instead of the latest one, leading to performance degradation.
C) The documents are in an unsupported language or exceed the maximum page length of 125 pages, causing extraction failures or inaccuracies.
D) The default expiration time for the 'GET PRESIGNED URL' function is causing some URLs to expire before the Document AI model can process them.
E) The role lacks the 'EXECUTE TASK' privilege, preventing the scheduled pipeline tasks from running.

5. A multinational corporation is implementing Document AI to automate the processing of purchase orders from various global suppliers. These purchase orders vary significantly in layout and are often submitted in English, German, and Spanish. The data engineering team aims to optimize the preparation phase for effective model training and deployment. Considering Document AI's 'Question optimization best practices' and general document preparation guidelines, which of the following is a 'critical consideration' for successful implementation?

A) The training dataset should be highly diverse, representing various layouts, data variations (including potential NULLs), and containing documents in all target languages (English, German, Spanish) to ensure robust model performance.
B) To simplify prompt engineering, define generic questions such as 'What is the total amount?' irrespective of document layout, as Document AI's foundation model is expected to handle most layout variations through its zero-shot capabilities.
C) For multilingual support, it is mandatory to externally translate all non-English purchase orders to English before uploading them to an internal stage, as
D) For maximum efficiency in defining data values for complex extractions (e.g., lists of line items), prioritize spending extensive time on crafting highly precise and detailed natural language prompts to guide the model.
E) Document AI model builds can only support a single document layout type; therefore, separate model builds must be created for each distinct purchase order layout from different suppliers.

Solutions: