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Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A company is developing a Streamlit application leveraging Snowflake Cortex Analyst for natural language querying over sales data.
They want to implement a robust multi-turn conversational experience where users can ask follow-up questions. Which of the following statements accurately describe the design and cost implications of supporting multi-turn conversations in Cortex Analyst? (Select all that apply)

A) When an LLM judge is used to evaluate the summarization quality for multi-turn conversations, a smaller model like Llama 3.1 8B is generally preferred over Llama 3.1 70B to minimise latency, even if it leads to a slightly higher error rate in rewritten questions.
B) Developers can manually implement multi-turn conversations in their applications by using the
C) The cost for Cortex Analyst's multi-turn conversational support is primarily based on the number of messages processed, and the number of tokens within each message does not directly affect the per-message cost.
D) Cortex Analyst supports multi-turn conversations by simply passing the entire conversation history directly to every LLM call within its agentic workflow, which is the most efficient method for maintaining context.
E) An internal LLM summarization agent is automatically employed by Cortex Analyst before its original workflow to reframe follow-up questions based on conversation history, optimising LLM processing for each agent.

2. A Snowflake administrator needs to implement a granular access control strategy for LLMs. The general policy is to restrict access to a select few models via an account-level allowlist. However, a specific data science team (using role 'DATA SCIENCE TEAM ROLE) requires access to the 'claude-3-5-sonnet' model, which should not be available to other users or globally via the allowlist. Given this scenario, which set of commands would correctly establish this access control while adhering to the specified requirements?

A)

B)

C)

D)

E)

3. A development team is implementing a document retrieval system in Snowflake. They plan to store document embeddings and use VECTOR_L2_DISTANCE to find the most relevant documents for a given query embedding. Considering Snowflake's capabilities, which of the following statements are true regarding the use of vector types and VECTOR_L2_DISTANCE
? (Select all that apply)

A) O When defining a table column for 1024-dimensional float embeddings, the SQL type specification
B) To prevent issues with direct vector comparisons, explicitly using
C) Document embeddings, which are typically float arrays, can be stored in a
D) Using the Snowpark Python library, developers can directly invoke

E) VECTOR

4. A data engineering team is preparing a large corpus of unstructured text documents for a Retrieval Augmented Generation (RAG) application in Snowflake, leveraging Cortex Search and LLM functions. They plan to use SNOWFLAKE.CORTEX.SPLIT_TEXT_RECURSIVE_CHARACTER as part of their data ingestion pipeline. What is the primary benefit of employing this helper function in the context of their RAG workflow?

A) It performs sentiment analysis on each chunk, allowing the RAG system to filter out negative or irrelevant content before retrieval.
B) It automatically translates documents into a target language, ensuring multilingual compatibility for the LLM.
C) It compresses the text data to reduce storage costs in Snowflake stages before processing by embedding models.
D) It generates vector embeddings for each document chunk, eliminating the need for separate embedding models.
E) It divides lengthy documents into smaller, manageable text chunks, which improves the precision of information retrieval and the relevance of downstream LLM responses.

5. A multi-national corporation uses Snowflake across several AWS regions. Their primary operational Snowflake account is in AWS US East (Ohio), but they need to leverage a specific AI_COMPLETE model, llama4-maverick, which is natively available in AWS US East 1 (N. Virginia) but not in US East (Ohio). To address this, the Snowflake administrator enables cross-region inference for their US East (Ohio) account.

A) User inputs, service-generated prompts, and the generated outputs from cross-region AI_COMPLETE calls are automatically stored or cached in the remote processing region to optimize performance for subsequent identical requests.
B) To enable cross-region inference for the US East (Ohio) account, the administrator would execute the command: ALTER ACCOUNT SET AWS_US' ; to allow inference requests to be processed in any AWS US region where the model is available. CORTEX_ENABLED_CROSS REGION =AWS_US' ;
C) The query latency for cross-region inference with AI_COMPLETE is consistently low and predictable, as Snowflake's architecture is designed to completely negate the impact of geographical distance and network variations.
D) Cross-region inference is fully supported for AI_COMPLETE in U.S. SnowGov regions for both inbound and outbound inference requests, provided the target model is available in the respective SnowGov region.
E) The llama4-maverick model is listed as natively available in AWS US East 1 (N. Virginia) and is supported for cross-region inference (AWS US Cross-Region), validating it as a suitable target for inference from US East (Ohio).

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