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Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A data science team is deploying a custom real-time inference service for a fine-tuned LLM using Snowpark Container Services (SPCS). They have a Docker image in their Snowflake image repository. They need to define the service using a YAML specification file. Which of the following are ''essential'' components or configurations that must be included in the 'spec.yaml'' file for a long- running service that uses this image, custom environment variables, and requires external access?

A)

B)

C)

D)

E)

2. A data engineering team has developed a Python-based generative AI application and instrumented its key functions using the TruLens SDK. Their next step is to register this application with Snowflake AI Observability to initiate evaluation runs and capture application traces within Snowflake.

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

3.
Which of the following SQL snippets, when executed against a single invoice file like "invoice001 .pdf", correctly extracts and transforms the desired data, assuming 'json_content' holds the raw Document AI output?

A)

B)

C)

D)

E)

4. An analytics engineering team is building a complex, real-time data pipeline in Snowflake. They want to automatically summarize new incoming product reviews using SNOWFLAKE. CORTEX. SUMMARIZE as part of a continuous process. They consider integrating this function into a dynamic table definition for efficient, automated refreshes. Which of the following statements regarding the integration of SNOWFLAKE. CORTEX. SUMMARIZE with Snowflake's data pipeline features is true?

A) SNOWFLAKE. CORTEX. SUMMARIZE can be directly used within a dynamic table's SELECT statement, enabling real-time summarization with automated refreshes, provided the source data is a stream.
B) While SNOWFLAKE. CORTEX. SUMMARIZE does not support dynamic tables, its updated version, is fully compatible with dynamic tables for continuous summarization.
C) SNOWFLAKE. CORTEX. SUMMARIZE can be used with dynamic tables if the TARGET_LAG is explicitly set to '1 day' or longer, allowing for asynchronous processing.
D) Snowflake Cortex functions, including SNOWFLAKE. CORTEX.SUMMARIZE, are explicitly incompatible with dynamic tables, requiring alternative automation methods such as streams and tasks.
E) SNOWFLAK CORTEX. SUMMARIZE is supported in dynamic tables only if a Snowpark-optimized warehouse is explicitly assigned to the dynamic table for processing.

5. data scientist is designing a Retrieval Augmented Generation (RAG) system in Snowflake for querying a large knowledge base of internal documents. They plan to store document embeddings and use vector similarity for retrieval. Which statement accurately describes the use of vector functions and associated costs in this RAG architecture?

A) The

B)

C) Storing vector embeddings, created using functions like

D) When using

E) Snowpark Python fully supports all Snowflake vector similarity functions, including

Solutions: