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Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A team is developing a Retrieval Augmented Generation (RAG) pipeline in Snowflake, where document chunks are embedded using Cortex AI functions and then retrieved using VECTOR_COSINE_SIMILARITY They are planning their infrastructure and cost management strategy. Which of the following statements correctly describes the cost or performance characteristics of these operations in Snowflake? (Select all that apply)

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

2. A data scientist wants to fine-tune a
mistral -7b
model to improve its ability to generate specific product descriptions based on brief input features. They have a table named PRODUCT_CATALOG with columns PRODUCT_FEATURES (text) and GENERATED_DESCRIPTION (text). Which of the following statements correctly describe the preparation and initiation of this fine-tuning job in Snowflake Cortex?
(Select all that apply)

A) O To generate highly structured
B) The SQL query for the training data must select columns aliased as
C) The
D) Once a fine-tuned model is created, it is fully managed by the Snowflake Model Registry API, allowing for programmatic updates to its parameters and versions.
E) The fine-tuning job must be created using a

3. 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) Increase the virtual warehouse size to a Large or X-Large to speed up processing and prevent URL expiration.
B) Grant the

C) Implement a mechanism to process documents in smaller batches or extend the expiration time for the presigned URLs to ensure timely access by Document
D) Reconfigure the external stage to use
E) 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.

4. An ML engineer is developing a RAG application in Python and wants to use the TruLens SDK to trace the distinct phases of its execution, specifically the context retrieval and answer generation steps. They aim to clearly differentiate the tracing of the function responsible for retrieving context.

A)

B)

C)

D)

E)

5. A data application developer is tasked with building a multi-turn conversational AI application using Streamlit in Snowflake (SiS) that leverages the COMPLETE (SNOWFLAKE. CORTEX) LLM function. To ensure the conversation flows naturally and the LLM maintains context from previous interactions, which of the following is the most appropriate method for handling and passing the conversation history?

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

Solutions: