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Snowflake GES-C01 Exam Syllabus Topics:

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A financial services company is developing an automated data pipeline in Snowflake to process Federal Reserve Meeting Minutes, which are initially loaded as PDF documents. The pipeline needs to extract specific entities like the FED's stance on interest rates ('hawkish', 'dovish', or 'neutral') and the reasoning behind it, storing these as structured JSON objects within a Snowflake table. The goal is to ensure the output is always a valid JSON object with predefined keys. Which AI_COMPLETE configuration, used within an in-line SQL statement in a task, is most effective for achieving this structured extraction directly in the pipeline?

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

2. 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)

3. A data scientist has fine-tuned a Hugging Face sentence transformer model for semantic search and intends to deploy it to Snowpark Container Services (SPCS) via the Snowflake Model Registry. The model requires GPU acceleration and specific Python packages ('sentence-transformerS, 'torch', 'transformers'). A GPU compute pool named 'my_gpu_pool' is available. Which of the following code snippets correctly logs the model and deploys it as a service to SPCS, ensuring it utilizes the GPU compute pool and has the necessary Python dependencies for the Hugging Face model and PyTorch?

A)

B)

C)

D)

E)

4. A data engineering team is building a pipeline to process legal documents using Snowflake Cortex functions. They aim to extract specific entities and summarize key clauses while being highly cost-conscious. To optimize token-based costs, which of the following practices should they implement when using Cortex LLM functions?

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

5. A data science team is implementing a large-scale Retrieval Augmented Generation (RAG) application on Snowflake, using 'SNOWFLAKE.CORTEX.EMBED TEXT 1024' to process millions of customer support tickets for semantic search. The goal is to achieve high retrieval quality and manage costs effectively. Which of the following are recommended practices and accurate cost/performance considerations when leveraging 'EMBED TEXT 1024' in this scenario? (Select all that apply)

A) Models for such as 'snowflake-arctic-embed-l-v2.0' and 'multilingual-e5-large' , are billed at 0.05 Credits per one million input tokens processed.
B) For 'EMBED_TEXT 1024' , billing is based on both input and output tokens, encouraging brevity in generated embeddings to control costs.
C) Even with models like 'snowflake-arctic-embed-l-v2.0-8k' which have a large context window (8192 tokens), splitting customer support tickets into chunks of no more than 512 tokens is recommended for optimal RAG retrieval quality.
D) The function should be called using 'TRY_COMPLETE instead of directly to handle potential errors gracefully and avoid incurring costs for failed operations.
E) To minimize compute costs, the team should use a Snowpark-optimized warehouse for operations, as it is specifically designed for ML workloads.

Solutions: