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Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. An operations manager is tasked with monitoring the cost and ensuring compliance for a Cortex Analyst deployment that uses the REST API. They are particularly concerned with accurately tracking credit consumption and understanding the implications of enabling external models. Which of the following statements correctly describe aspects of Cortex Analyst cost and governance?

A) Enabling Azure OpenAI models via the ' ENABLE_CORTEX_ANALYST_MODEL_AZURE_OPENAF parameter ensures that all customer data and prompts remain within Snowflake's governance boundary and fully respect RBAC policies for those models.
B) Credit consumption for Cortex Analyst is primarily based on the number of tokens processed by the underlying LLMs, with more complex natural language questions leading to higher token usage and costs.
C) The view can be queried to track detailed information about cortex Analyst requests, including generated SQL and any errors.
D) Snowflake's view provides granular usage information for REST API requests to cortex Analyst, including tokens processed per model.
E) Cortex Analyst credit usage is based on the number of messages processed, at a rate of 67 Credits per 1 ,000 messages, and only successful responses (HTTP 200) are counted.

2. A global analytics firm is developing a Retrieval Augmented Generation (RAG) system in Snowflake to answer customer queries across a large repository of technical documentation, which includes documents in English, German, and Spanish. They are looking to use a Snowflake Cortex embedding model to convert document chunks into vector embeddings for their Cortex Search Service. Which of the following considerations are critical when selecting an appropriate embedding model to optimize for both query relevance and cost-efficiency for their multilingual RAG application? (Select all that apply)

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

3. An AI developer is testing a new RAG application in Snowflake. The application uses

in this scenario?

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

4. A data engineer is integrating SNOWFLAKE. CORTEX. CLASSIFY_TEXT into an automated data pipeline that uses dynamic tables to process and transform streaming text dat a. They have ensured that the service account used has been granted the necessary SNOWFLAKE. CORTEX_USER database role. After deploying the pipeline, they consistently receive an error whenever CLASSIFY_TEXT is invoked. Which of the following is the most likely cause of the error encountered by the data engineer?

A) Snowflake Cortex functions, including 'CLASSIFY_TEXT , currently do not support integration with dynamic tables within data pipelines.
B) The role used by the data engineer, despite having 'SNOWFLAKE.CORTEX_USER, lacks the fundamental 'USAGE privilege on the database where the text data is stored.
C) The array contains more than 100 unique categories, exceeding the maximum allowed limit for the function.
D) The 'task_description' provided in the optional arguments for 'CLASSIFY_TEXT exceeds the recommended length of approximately 50 words, leading to a validation error.
E) The input text being processed by 'CLASSIFY _ TEXT includes extensive non-plain English content, such as code blocks, which causes the function to fail with an error.

5. A financial institution uses Snowflake Cortex Analyst with strict role-based access control (RBAC) on their Snowflake-hosted LLMs. The security team has granted specific 'CORTEX-MODEL-ROLE application roles to different analyst teams, ensuring they only access approved models. A new requirement arises to enable Azure OpenAI GPT models for Cortex Analyst to leverage a specific feature. An administrator proceeds to execute:

Which of the following statements accurately describe the implications of this change?

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

Solutions: