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Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A Gen AI Specialist is leveraging Snowflake Document AI to extract specific entities and table data from a large and varied collection of documents. They are aware of potential limitations and want to understand the expected outcomes when processing different types of files. Considering a scenario where a Document AI model build is used with the '!PREDICT' method, which of the following statements accurately describe the expected behavior or potential issues based on Document AI's conditions and limitations?

A) If a question for an entity, like 'total_invoice_amount', does not find a corresponding value in a document, the JSON output for will contain a 'value' key with a 'null' string and a 'score' key indicating the model's confidence in the absence of the answer.
B) In a table extraction task, if a specific cell (e.g., 'tablellitem') is empty, the resulting JSON will omit the 'value' key for that cell, but will still provide a 'score' indicating the model's confidence that the cell is empty.
C) A document written entirely in Ukrainian will be processed by Document AI, and the extracted information will be of satisfactory quality due to extensive multilingual support.
D) Processing a legal contract document that is 130 pages long will likely result in a '_processingErrors' message indicating that the document has too many pages.
E) If the extracted answer to a question for a single entity (e.g., is very long, it will be automatically truncated to a maximum of 2048 tokens.

2. A Snowflake Gen AI Specialist is defining a semantic model for Cortex Analyst to improve text-to-SQL accuracy. They are adding entries to the verified _ queries section of their YAML file. Consider the following semantic model snippet and a proposed verified_query entry. Which of the following statements correctly identifies an issue or a best practice not followed in the sql field of the proposed verified_query entry, based on Cortex Analyst VQR guidelines? Semantic Model Snippet:

Proposed verified _ query entry:

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

3. A data engineering team is building an automated pipeline in Snowflake to process customer reviews. They need to use AI_COMPLETE to extract specific details like product, sentiment, and issue type, and store them in a strictly defined JSON format for seamless downstream integration. They aim to maximize the accuracy of the structured output and manage potential model limitations. Which statements accurately reflect the best practices and characteristics when using AI_COMPLETE with structured outputs for this scenario?

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

4. An analytics team is preparing documents for a new Document AI model build to extract information from internal policy reviews. They have a variety of documents that they intend to upload to an internal stage for processing. The document list includes: (1) a 70 MB PDF with 100 pages, (2) a 45 MB DOCX with 150 pages, (3) a 30 MB PNG image, (4) a 60 MB TIFF image, and (5) a 20 MB HTML file. All documents are in English. Which of these documents would 'fail' to meet the direct input requirements for Document AI processing?

A) The 20 MB HTML file.
B) The 30 MB PNG image.
C) The 45 MB DOCX with 150 pages.
D) The 60 MB TIFF image.
E) The 70 MB PDF with 100 pages.

5. A data engineer is tasked with establishing a robust MLOps pipeline using the Snowflake Model Registry. They have trained a scikit-learn model and need to log it. Which of the following statements correctly describes a 'required' step or privilege for successfully logging a model using the 'Registry.log_model' method?

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

Solutions: