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Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A data engineering team is optimizing an AI-infused pipeline that processes millions of rows of customer interaction data in a LOG_DATA table using various Snowflake Cortex AI functions. They need to accurately estimate costs and ensure optimal performance. Which of the following statements regarding cost, performance, and operational considerations for these functions are true?

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

2.

A) D If 'audit_status' is not found, the 'audit_status' key will be entirely absent from the JSON output for that document.
B) The 'audit_statuS field, if not found, will appear in the JSON output as Taudit_status": [{"score": 0.0, "value": null}]}'.
C) If a 'quantity' cell in is empty, the JSON output for that specific cell will include a 'score' key but omit the 'value' key.
D) For successfully extracted entities like 'net_profit' , the JSON output typically includes an array of objects, each containing both 'score' and 'value' keys.
E) The '_documentMetadata.ocrScore' field will always be present in the root of the JSON output, indicating the confidence in the optical character recognition process for the entire document.

3. An engineering team is building an advanced analytics pipeline where daily customer activity vectors (each 512 dimensions) are compared against a set of known activity patterns using VECTOR_L2_DISTANCE The pipeline is orchestrated using Snowflake Tasks. Which operational best practice or limitation should the team consider when processing these vector distances at scale?

A) Snowflake recommends using smaller warehouses (no larger than MEDIUM) for executing queries that call Cortex AI SQL functions, as larger warehouses do not necessarily increase performance, and this guidance extends to vector similarity functions.
B) When embedding external data into the Snowflake environment for

C) The
D) For optimal performance of
E) To handle potential errors from

4. A developer is refining a Document AI extraction process using the '!PREDICT' method and is meticulously examining the JSON output for invoices, which include 'invoice number', 'invoice items', 'tax amount', and 'vendor name'. They also have a detailed internal table of 'product details' to be extracted. To ensure optimal data quality and accurate interpretation of the extracted information, which of the following best practices or characteristics of Document AI's output should the developer consider?

A) For table extraction, such as the extracted values for each column (e.g., 'tablel litem', 'tablel Igross) are ordered consistently with the rows of the original table, facilitating direct joining of columns.
B) To maximize accuracy when defining data values, questions should be broadly generic (e.g., 'What is the amount?) to allow the Document AI model to infer the most relevant context, especially for fields like 'tax_amount' where multiple numbers might be present.
C) If the 'vendor_name' field cannot be confidently identified in a document, the model will include '"vendor_name": [ { "score": O.X, "value": "NOT FOUND" } l' in the JSON output.
D) The 'ocrScore' provided in the '_documentMetadata' object for each document indicates the model's confidence in the content of specific extracted values, rather than the overall quality of the optical character recognition process.
E) When extracting lists of values, such as 'invoice_items', the Document AI model returns them as an array in the JSON output, preserving the original order of items as they appear in the document.

5. A data engineering team is setting up a Retrieval Augmented Generation (RAG) application using Snowflake Cortex Search to provide contextual answers from customer support transcripts. The transcripts are stored in a Snowflake table named SUPPORT _ TRANSCRIPTS. Which of the following statements are crucial considerations or accurate facts regarding the initial setup and configuration of the Cortex Search Service for this use case?

A) Snowflake recommends using a dedicated virtual warehouse of any size, including X-Large or 2X-Large, for each Cortex Search Service to ensure the fastest possible materialization of search indexes during creation and refresh.
B) The CREATE CORTEX SEARCH SERVICE command requires that CHANGE_TRACKING = TRUE be enabled on the source table, especially if the role creating the service is not the table owner. This ensures that the service can track updates to the base data.
C) Cortex Search is designed to get users up and running quickly with a hybrid (vector and keyword) search engine on text data, handling embedding, infrastructure maintenance, and search quality parameter tuning automatically.
D) Columns specified in the ATTRIBUTES field during service creation are only used for filtering search results and do not need to be present in the source query.
E) The Cortex Search Service can effectively be used as a RAG engine for LLM chatbots by leveraging semantic search capabilities to provide customized and contextualized responses from the text data.

Solutions: