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Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. An enterprise is deploying a new RAG application using Snowflake Cortex Search on a large dataset of customer support tickets. The operations team is concerned about managing compute costs and ensuring efficient index refreshes for the Cortex Search Service, which needs to be updated hourly. Which of the following considerations and configurations are relevant for optimizing cost and performance of the Cortex Search Service in this scenario?

A) For optimal performance and cost efficiency, Snowflake recommends using a dedicated warehouse of size no larger than MEDIUM for each Cortex Search Service.
B) For embedding text, selecting a model like

C) The primary cost driver for Cortex Search is the number of search queries executed against the service, with the volume of indexed data (GB/month) having a minimal impact on overall billing.
D) The

E) CHANGE_TRACKING

2. A financial institution is building a Document AI model to process loan applications. They need to extract all applicant names (which can be multiple, forming a list), classify the application as 'Approved' or 'Rejected' based on various internal criteria (not explicitly stated in the document), and normalize diverse date formats (e.g., '1st Jan 2023', '01/01/2023') to 'YYYY-MM-DD'. Which of the following approaches represent 'best practices' for defining data values and training the Document AI model for these complex extractions? (Select ALL that apply.)

A) When defining data values for combinations of values, arrays, or nonstandard formats, fine-tuning the model with annotations that 'show' the expected result is generally more effective than 'telling' it via complex prompt engineering.
B) To normalize diverse date formats to 'YYYY-MM-DD', train the model with sufficient annotations, showing the desired output format for various input date formats, allowing Document AI to learn the normalization internally.
C) For extracting lists of items from tables, use individual data values for each column and then merge these lists in the pipeline, ensuring adequate training with sample data, including NULL values and correct order, for reconstruction.
D) To classify applications as 'Approved' or 'Rejected', define a data value like 'What is the application status?' and ensure the training dataset includes annotated examples for both 'Approved' and 'Rejected' classes.
E) For applicant names, define a single value 'applicant_name' and rely solely on advanced prompt engineering to instruct the model on how to identify and list all names, without providing specific examples through annotations.

3. A data scientist has successfully deployed a Hugging Face sentence transformer model to Snowpark Container Services (SPCS) for GPU-powered inference, making it accessible via an HTTP endpoint. To ensure secure and proper programmatic access to this service from an external application, which of the following statements correctly describe the authentication and access control considerations for calling this public endpoint?

A) The Python API for calling the service requires the Snowflake session object directly, bypassing HTTP endpoint authentication.
B) The 'Authorization' header with a 'Snowflake Token=""' value is a valid method for authenticating requests to the public endpoint programmatically.
C) The default role for the calling user must have the 'SNOWFLAKCORTEX USER database role granted to access the SPCS service via its public endpoint.
D) Applications must use key pair authentication to generate a JSON Web Token (JWT), exchange it with Snowflake for an OAuth token, and then use this OAuth token to authenticate requests to the public endpoint.
E) The public endpoint of the SPCS service can be accessed directly without any authentication, as it's a public endpoint.

4. A Gen AI engineer is tasked with selecting the most suitable Large Language Model (LLM) from Snowflake Cortex AI for a new customer service chatbot. They need to rapidly prototype and compare different LLMs with varying parameters on a sample dataset before committing to a production deployment. Which of the following statements accurately describe how the Cortex Playground (Public Preview) can assist in this scenario?

A) It allows direct fine-tuning of selected LLMs with custom datasets within the playground interface to improve model performance for specific tasks.
B) It enables side-by-side comparison of model outputs for different LLMs and model settings, facilitating an informed decision on model selection.
C) It supports exporting the tested prompts and model configurations as Python code, ready for integration into a Snowpark ML pipeline.
D) It allows connection to a Snowflake table with textual data, processing up to 100 rows, to experiment with prompts directly on actual data.
E) It provides a mechanism to deploy the chosen LLM directly into Snowpark Container Services (SPCS) compute pools from within the playground for immediate production use.

5. An operations manager is concerned about the governance and monitoring of a newly deployed Cortex Analyst application, especially regarding cost tracking and how multi-turn conversations are handled. Which statement accurately describes a feature for monitoring or managing a Cortex Analyst application?

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

Solutions: