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Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A Snowflake account is located in the AWS US East 1 (N. Virginia) region. The 'ACCOUNTADMIN' has set the 'CORTEX MODELS ALLOWLIST' to "mistral-7b" and 'CORTEX ENABLED CROSS REGION' to 'ANY REGION'. A data scientist, whose role has only the 'SNOWFLAKE.CORTEX USER database role, performs several 'AI COMPLETE calls. Which of the following statements correctly describe the behavior of these calls under the given configuration?

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

2. A business analyst is using a Cortex Analyst-powered conversational application to query structured data in Snowflake. They initially ask, 'What was the total profit from California last quarter?' and then follow up with, 'What about New York?' The application successfully provides accurate answers to both questions. Which of the following statements explain how Cortex Analyst supports this multi-turn conversational experience and maintains accuracy? (Select all that apply)

A) Cortex Analyst stores the full, verbatim history of all previous user prompts and LLM responses, which are then passed to every subsequent LLM call to ensure complete context retention without any summarization.
B) The accuracy of the SQL queries generated by Cortex Analyst for follow-up questions is significantly enhanced by its integration with a Verified Query Repository (VQR), which stores pre-verified natural language questions and their corresponding SQL queries.
C) The semantic model YAML file, which defines logical tables, dimensions, and measures, is crucial for Cortex Analyst to bridge the gap between business terminology and underlying technical schema, thereby improving text-to-SQL conversion accuracy for both initial and follow-up queries.
D) For multi-turn conversations, Cortex Analyst primarily relies on semantic search over sample values defined in the semantic model to infer context and generate SQL, making explicit conversation history management unnecessary.
E) To handle follow-up questions, Cortex Analyst leverages an internal LLM summarization agent (e.g., Llama 3.1 70B) to reframe the current-turn question by retrieving context from the conversation history, rather than simply passing the entire history.

3. An ML Engineer has successfully deployed a custom text embedding model, 'my_embedder model', to a Snowpark Container Service named 'text embedding_service' within their Snowflake account. This model has an 'encode' method that accepts a string and returns a vector. They now need to integrate inference calls from this deployed model into various applications. Which of the following are valid ways to invoke this model for inference?

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

4. A Snowflake team observes consistently high token costs from 'SNOWFLAKE.ACCOUNT_USAGE.CORTEX_FUNCTIONS_QUERY_USAGE_HISTORY' for a summarization task using the 'mistral- large2' model. The task involves summarizing legal documents, which often exceed the context window of common LLMs. To optimize these token-based costs, which strategy should the team prioritize?

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

5. A Gen AI specialist is preparing to upload a large volume of diverse documents to an internal stage for Document AI processing. The objective is to extract detailed information, including lists of items and potentially classifying document types, and then automate this process. Which of the following statements represent 'best practices or important considerations/limitations' when preparing documents and setting up the Document AI workflow in Snowflake? (Select ALL that apply.)

A) If the Document AI model does not find an answer for a specific field, the '!PREDICT method will omit the 'value' key but will still return a 'score' key to indicate confidence that the answer is not present.
B) To improve model training, documents uploaded should represent a real use case, and the dataset should consist of diverse documents in terms of both layout and data.
C) For continuous processing of new documents, it is best practice to create a stream on the internal stage and a task to automate the '!PREDICT method execution.
D) When defining data values for extraction, especially for nonstandard formats or combinations of values, fine-tuning the model with annotations is generally more effective than relying solely on complex prompt engineering.
E) Documents with a page count exceeding 125 pages or a file size greater than 50 MB will be processed, but with a potential reduction in extraction accuracy.

Solutions: