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Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A financial analyst wants to build a generative AI application in Snowflake that can answer complex queries by integrating financial reports (unstructured data in stages) and transaction records (structured data in tables). They decide to use Snowflake Cortex Agents. Which of the following statements accurately describe the capabilities and operational aspects of Cortex Agents in this scenario?
(Select all that apply)

A) To provide the Agent with custom logic for specific data transformations not covered by standard tools, stored procedures or user-defined functions (UDFs) can be implemented as custom tools.
B) Cortex Agents are designed to orchestrate tasks by planning steps, utilising tools like Cortex Analyst for structured data and Cortex Search for unstructured data, and generating comprehensive responses.
C) For monitoring agent interactions and performance on the client application, the TruLens Python packages (

D) When a user asks an ambiguous question, Cortex Agents utilise an 'Explore options' component to consider different permutations and disambiguate the query for improved accuracy.
E) The primary compute cost for Cortex Agents is based on the number of tokens processed during the planning and reflection phases, with an additional per- message charge for each tool invocation.

2. A data application developer is building a Streamlit chat application within Snowflake. This application uses a RAG pattern to answer user questions about a knowledge base, leveraging a Cortex Search Service for retrieval and an LLM for generating responses. The developer wants to ensure responses are relevant, concise, and structured. Which of the following practices are crucial when integrating Cortex Search with Snowflake Cortex LLM functions like AI_COMPLETE for this RAG chatbot?

A) Using the

B) The

C) For performance and cost optimization, it is always recommended to query Cortex Search and the LLM function within a single
D) The retrieved context from Cortex Search should be directly concatenated with the user's prompt as input to the
E) To maintain conversational context in a multi-turn chat, the developer should pass all previous user prompts and model responses in the

3. A financial data team is implementing a Snowflake Cortex AI solution to summarize regulatory documents using SNOWFLAKE.CORTEX.TRY_COMPLETE They aim for both cost efficiency and high reliability, especially when dealing with documents that might occasionally exceed model context limits or result in malformed output. Which of the following statements about the cost and operational behavior of TRY_COMPLETE are TRUE in this context? (Select all that apply)

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

4. A data analyst is tasked with identifying customers who purchased items with similar feature vectors. They have a table products with an

to measure similarity. Which of the following statements correctly describe aspects of defining and using vector types or functions in this scenario? (Select all that apply)

A) If
B) When inserting literal arrays as vectors into a table for comparison, explicit casting, e.g.,

C) Comparing two

D) The Snowpark Python library provides native support for calling
E) To correctly define a column to store 768-dimensional float embeddings, the SQL statement

5. A data engineer has successfully experimented with a prompt and various model settings in the Snowflake Cortex Playground for a text classification task using the mistral-large2 model and Cortex Guard. They now want to operationalize this solution within their Snowflake environment. Which of the following statements correctly describe capabilities or considerations when moving from the Cortex Playground to a production pipeline?

A) The exported SQL query, when used with dynamic tables, supports incremental refresh for efficient processing of new data without recomputing the entire table.
B) If the mistral-large2 model is not natively available in the target production region, cross-region inference must be enabled by setting the CORTEX_ENABLED_CROSS REGION parameter.
C) To filter unsafe LLM responses in production, the Cortex Guard option, which is built with Meta's Llama Guard 3, must be explicitly enabled in the COMPLETE function's options argument.
D) For continuous processing of new data, the exported SQL query can be automated using
E) The Playground allows exporting the exact SQL query with all defined model settings, including temperature and Cortex Guard enablement, for direct use in a Snowflake worksheet or task.

Solutions: