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Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. 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) VECTOR
B) Using the Snowpark Python library, developers can directly invoke

C) Document embeddings, which are typically float arrays, can be stored in a
D) O When defining a table column for 1024-dimensional float embeddings, the SQL type specification
E) To prevent issues with direct vector comparisons, explicitly using

2. A data scientist is implementing a Retrieval Augmented Generation (RAG) system in Snowflake for a legal document repository. They need to convert legal document chunks into vector embeddings and efficiently find the most relevant document chunks based on a user's query. Which of the following statements accurately describe the process and best practices for creating and using these vector embeddings with Snowflake Cortex LLM functions?

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

3. A Gen AI Specialist in Snowflake Cortex aims to fine-tune an LLM for enhanced task-specific performance. When creating a fine-tuning job using SNOWFLAKE. CORTEX. FINETUNE( 'CREATE', ... ) , which statement accurately describes the required training data format and a supported base model, aligning with Snowflake's Gen AI principles for leveraging LLMs?

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

4. A data science team is fine-tuning a Snowflake Document AI model to improve the extraction accuracy of specific fields from a new type of complex legal document. They are consistently observing low confidence scores and inconsistent 'value' keys for extracted entities, even after initial training. Which two of the following best practices should the team follow to most effectively improve the model's extraction accuracy and confidence for this complex document type?

A) Prioritize extensive prompt engineering by creating highly detailed and complex questions with intricate logic to guide the LLM's understanding of the extraction task.
B) Ensure the training dataset used for fine-tuning includes diverse documents representing various layouts, data variations, and explicit examples of values or empty cells where appropriate.
C) Set the 'temperature' parameter to a higher value (e.g., 0.7) during '!PREDICT calls to encourage more creative and diverse interpretations by the model.
D) Actively involve subject matter experts (SMEs) or document owners throughout the iterative process to help define data values, provide annotations, and evaluate the model's effectiveness.
E) Limit the fine-tuning training data exclusively to perfectly formatted and clean documents to ensure the model learns from ideal examples without noise.

5. A software development team is building a conversational AI application within Snowflake, aiming to provide a dynamic and stateful chat experience for users. The application needs to handle follow-up questions while maintaining context, provide responses with a degree of creative variation, and actively filter out any potentially harmful content. The team utilizes the SNOWFLAKE. CORTEX. COMPLETE (or AI_COMPLETE) function.

A)

B)

C)

D)

E)

Solutions: