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Snowflake SnowPro® Specialty: Gen AI Certification Exam Sample Questions (Q224-Q229):

NEW QUESTION # 224
A Snowflake developer, named ANALYST USER, is tasked with creating a Streamlit in Snowflake (SiS) application that will utilize both SNOWFLAKE. CORTEX. COMPLETE for generating responses and SNOWFLAKE. CORTEX.CLASSIFY_TEXT for categorizing user input. To ensure the role used by ANALYST USER has the necessary permissions for executing these Cortex LLM functions and operating within a specified database and schema, which of the following database roles or privileges must be granted? (Select all that apply.)

A.
B. The USAGE privilege on the database and schema where the Streamlit application runs and potentially stores related data.
C.
D.
E.

Answer: B,D

Explanation:
To execute Snowflake Cortex AI functions such as 'SNOWFLAKE.CORTEX.COMPLETE and 'SNOWFLAKE.CORTEX.CLASSIFY_TEXT , the role used by the developer must be granted the 'SNOWFLAKE.CORTEX_USER database role. This role provides the necessary permissions to call these specific AI functions. Additionally, for a Streamlit application to run and perform operations within a designated database and schema (e.g., accessing tables, stages, or storing outputs), the role requires the 'USAGE privilege on that database and schema. Option B ('CREATE SNOWFLAKE.ML.DOCUMENT INTELLIGENCE') is a privilege specifically for creating DocumentAI model builds, not for using general Cortex LLM functions. Option D (EXECUTE TASK) is required for creating and running tasks, typically in automated data pipelines, which is distinct from direct LLM function execution within a Streamlit app. Option E is an application role necessary for AI Observability to log and view application traces for debugging and performance evaluation, but it is not a core requirement for merely executing LLM functions.

NEW QUESTION # 225
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. Increase the 'max_tokenS parameter within the ' !PREDICT' function options to accommodate longer document responses from the model.
B. Scale down the virtual warehouse to 'X-SMALC or 'SMALL' size, as larger warehouses do not increase Document AI query processing speed and incur unnecessary costs.
C. Redefine extraction questions to be more generic and encompassing, reducing the number of distinct questions needed per document.
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'Y.

Answer: D,E

Explanation:
The error messages 'Document has too many pages. Actual: 130. Maximum: 125.' and File exceeds maximum size. Actual: 54096026 bytes. Maximum: 50000000 bytes.' directly indicate that the documents do not meet Document AI's input requirements, which specify a maximum of 125 pages and 50 MB file size. Therefore, implementing a pre-processing step to split or resize these documents is an essential solution (Option B). The error 'cannot identify image file <_io.Bytesl0 object at Ox...>' is a known issue that occurs when an internal stage used for Document AI is not configured with 'SNOWFLAKE_SSE encryption. Correctly configuring the stage with this encryption type is crucial for resolving this processing error (Option D). Option A, while addressing cost optimization, is not a root cause of the 'processing errors' themselves, although it is a best practice for cost governance as larger warehouses do not increase Document AI query processing speed. Option C is incorrect; best practices for question optimization suggest being specific, not generic. Option E is incorrect as 'max_tokenS relates to the length of the model's output, not the input document's size or page limits.

NEW QUESTION # 226
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. 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.
B. 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.
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. 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.

Answer: A,B,D,E

Explanation:

NEW QUESTION # 227
A financial services company is developing an automated data pipeline in Snowflake to process Federal Reserve Meeting Minutes, which are initially loaded as PDF documents. The pipeline needs to extract specific entities like the FED's stance on interest rates ('hawkish', 'dovish', or 'neutral') and the reasoning behind it, storing these as structured JSON objects within a Snowflake table. The goal is to ensure the output is always a valid JSON object with predefined keys. Which AI_COMPLETE configuration, used within an in-line SQL statement in a task, is most effective for achieving this structured extraction directly in the pipeline?

A. Option D
B. Option E
C. Option A
D. Option C
E. Option B

Answer: D

Explanation:
To ensure that LLM responses adhere to a predefined JSON structure, the 'AI_COMPLETE function's 'response_format' argument, which accepts a JSON schema, is the most effective and direct method. This mechanism enforces the structure, data types, and required fields, significantly reducing the need for post-processing and ensuring deterministic, high-quality output. The AI-Infused Data Pipelines with Snowflake Cortex blog highlights asking the LLM to create a JSON object for maximizing utility. While setting 'temperature' to 0 can improve consistency, it does not enforce a specific schema. Prompt engineering (Option A) can help but does not guarantee strict adherence. Using multiple extraction calls (Option D) is less efficient and robust for extracting multiple related fields than a single 'AI_COMPLETE call with a structured output schema. Snowflake Cortex does not automatically infer and enforce a JSON schema without explicit configuration (Option E).

NEW QUESTION # 228
A financial analyst is concerned about the rising costs of their Document AI pipeline, which uses 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 C
B. Option D
C. Option E
D. Option A
E. Option B

Answer: E

Explanation:
Snowflake explicitly recommends using an X-Small, Small, or Medium warehouse for Document AI. Scaling up the warehouse does not increase the speed of query processing for Document AI but can lead to unnecessary costs. This directly addresses the scenario of a 'LARGE warehouse running continuously and contributing to high bills. Option A is incorrect because while 'METERING DAILY HISTORY is used for cost tracking, Document AI's service-side usage appears under 'AI_SERVICES , not 'WAREHOUSE_METERING' for the AI service component itself. 'WAREHOUSE METERING' would show general warehouse costs, not specifically tied to Document AI's compute portion. Option C is incorrect because Document AI (using ' !PREDICT) incurs 'AI Services compute' costs based on 'time spent actually using these resources' (8 Credits per hour of compute), not per token. Option D is not necessarily accurate guidance; SAI PARSE DOCUMENT is a separate Cortex AI SQL function for document processing, billed per page, while Document AI's '!PREDICT is part of a Document AI model build. Replacing it without a full re- evaluation of the workflow might not be optimal or directly cost-efficient for an established pipeline. Option E is incorrect because the view tracks Document AI processing activity, including '!PREDICT calls.

NEW QUESTION # 229
......

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