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Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. An ML engineer has developed a custom PyCaret classification model and wants to deploy it to Snowpark Container Services (SPCS) for inference using the Snowflake Model Registry. The model requires specific versions of pycaret' , 'scipy', and 'joblib'. The engineer also wants to make the service accessible via an HTTP endpoint. Which of the following Model Registry and service creation steps are 'most appropriate' for the ML engineer? (Select all that apply.)

A)

B)

C)

D)

E) Opt for warehouse deployment instead of SPCS, as PyCaret is not natively supported by Snowflake and managing its dependencies in SPCS would be overly complex compared to a warehouse.

2. A development team is implementing a document retrieval system in Snowflake. They plan to store document embeddings and use VECTOR_L2_DISTANCE to find the most relevant documents for a given query embedding. Considering Snowflake's capabilities, which of the following statements are true regarding the use of vector types and VECTOR_L2_DISTANCE
? (Select all that apply)

A) To prevent issues with direct vector comparisons, explicitly using
B) Document embeddings, which are typically float arrays, can be stored in a
C) Using the Snowpark Python library, developers can directly invoke

D) VECTOR
E) O When defining a table column for 1024-dimensional float embeddings, the SQL type specification

3. 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) 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.
B) 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.
C) 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.
D) 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.
E) For multilingual support, it is mandatory to externally translate all non-English purchase orders to English before uploading them to an internal stage, as

4. An operations team is investigating an issue with a generative AI application powered by Snowflake Cortex Analyst, where users reported unexpected behavior in generated SQL. To diagnose the problem, they examine the detailed event logs captured by Snowflake AI Observability. Which categories of information can they expect to find in these event tables to assist their investigation?

A) Real-time CPU and memory usage statistics for the Snowflake virtual warehouse executing the LLM inference.
B) The full text of the natural language questions submitted by the users.
C) The exact SQL queries that Cortex Analyst generated in response to user questions.
D) The complete request and response bodies associated with the application's execution steps.
E) Any error messages or warnings that occurred during the processing of the request.

5. An administrator is reviewing their Snowflake bill and observes higher than expected storage and cloud services compute costs for a newly deployed Cortex Search Service. They need to investigate these charges. Which of the following statements correctly explains how these specific costs are incurred or can be monitored for a Cortex Search Service?

A) The 'CORTEX_DOCUMENT_PROCESSING_USAGE_HISTORY view is the most appropriate tool to monitor Cortex Search storage and cloud services compute costs, as it tracks all ' Services usage.
B) Cloud Services compute costs for Cortex Search are always billed without any adjustments, regardless of the daily virtual warehouse compute costs, because they are considered serverless features.
C) Storage costs are incurred for both the materialized source query data and the search index data structures, and these costs can be estimated by materializing the source query into a table using the CORTEX_SEARCH_DATA_SCAN table function, and then examining the size of that table.
D) High cloud services compute costs for Cortex Search are primarily driven by the complexity of the embedding model selected and can be optimized by choosing a simpler model.
E) The 'CORTEX_SEARCH_DAILY_USAGE_HISTORY view provides detailed breakdowns of storage costs per TB and cloud services compute credits incurred, including the 10% daily warehouse cost adjustment.

Solutions: