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Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. An ML Engineer is logging a custom PyCaret model to the Snowflake Model Registry, with the intention of deploying it to Snowpark Container Services (SPCS) for GPU-powered inference. The PyCaret model is wrapped in a 'custom_model.ModelContext'. Which of the following statements correctly describe the considerations for the call and the model's environment?

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

2. An operations team at a company is implementing a robust governance framework to monitor and optimize the costs associated with their Snowflake Cortex LLM function usage. They need to identify which functions are driving the highest token consumption and overall credit usage to pinpoint areas for cost reduction. Which of the following monitoring tools or methods are appropriate for gaining these insights into Cortex LLM function costs and token consumption?

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

3. A Streamlit application developer wants to use AI_COMPLETE (the latest version of COMPLETE (SNOWFLAKE. CORTEX)) to process customer feedback. The goal is to extract structured information, such as the customer's sentiment, product mentioned, and any specific issues, into a predictable JSON format for immediate database ingestion. Which configuration of the AI_COMPLETE function call is essential for achieving this structured output requirement?

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

4. A data scientist is optimising a Cortex Analyst application to improve the accuracy of literal searches within user queries, especially for high-cardinality dimension values. They decide to integrate Cortex Search for this purpose. Which of the following statements are true about this integration and the underlying data types in Snowflake? (Select all that apply)

A) The cost for embedding data into a Cortex Search Service is primarily incurred per output token generated by the embedding model, as these represent the final vector embeddings, rather than input tokens.
B) Cortex Search Services, when configured as a source for Snowflake dynamic tables, automatically refresh their search index with continuous data updates, maintaining low-latency search results.
C) The "VECTOR data type in Snowflake, used to store embeddings generated for Cortex Search, is fully supported as a clustering key in standard tables and as a primary key in hybrid tables to accelerate vector similarity searches.
D) To integrate Cortex Search with a logical dimension, the semantic model YAML must include a block within the dimension's definition, specifying the service name and optionally a 'literal_column' .
E) For optimal RAG retrieval performance with Cortex Search, it is generally recommended to split text into chunks of no more than 512 tokens, even when using embedding models with larger context windows such as 'snowflake-arctic-embed-l-v2.0-8k'.

5. A Gen AI developer is implementing a Document AI solution to extract key fields from thousands of diverse PDF reports, which vary significantly in length and complexity. They use the '!PREDICT method with 'GET_PRESIGNED_URL' to process documents from an external stage. After initial testing, they observe two distinct types of errors in the query results:

for other, lengthy PDF files. Which two of the following actions should the developer take to resolve these issues?

A) Grant the

B) Increase the virtual warehouse size to a Large or X-Large to speed up processing and prevent URL expiration.
C) Reconfigure the external stage to use
D) Redesign the input documents to ensure they do not exceed 125 pages per file, or preprocess by splitting overly long documents into multiple smaller files.
E) Implement a mechanism to process documents in smaller batches or extend the expiration time for the presigned URLs to ensure timely access by Document

Solutions: