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Snowflake GES-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Cortex Analyst and Semantic Layer	20-25%	- Performance tuning for analytical queries - Business logic implementation in semantic models - Semantic model design and configuration - Text-to-SQL translation and optimization
Data Preparation for Gen AI	15-20%	- Unstructured data handling - Vector stores and embeddings in Snowflake - Document processing and chunking strategies - Data governance for AI workloads
Generative AI Fundamentals and Concepts	20-25%	- Fine-tuning vs. retrieval approaches - LLM fundamentals and architectures - Retrieval-Augmented Generation (RAG) concepts - Prompt engineering principles - Vector embeddings and similarity search
Snowflake Cortex AI Capabilities	25-30%	- Model selection and cost optimization - Cortex AI functions and features - Secure data handling in AI workflows - Snowflake Copilot integration - COMPLETE function usage and parameters
Architecture and Best Practices	10-15%	- Cost management strategies - LLM pipeline architecture design - Security and privacy considerations - Performance optimization techniques - Monitoring and evaluation frameworks

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

A)

B)

C) Data for all these operations remains within Snowflake's governance boundary.
D)

E)

2. A Gen AI specialist is preparing to upload a large volume of diverse documents to an internal stage for Document AI processing. The objective is to extract detailed information, including lists of items and potentially classifying document types, and then automate this process. Which of the following statements represent 'best practices or important considerations/limitations' when preparing documents and setting up the Document AI workflow in Snowflake? (Select ALL that apply.)

A) For continuous processing of new documents, it is best practice to create a stream on the internal stage and a task to automate the '!PREDICT method execution.
B) If the Document AI model does not find an answer for a specific field, the '!PREDICT method will omit the 'value' key but will still return a 'score' key to indicate confidence that the answer is not present.
C) When defining data values for extraction, especially for nonstandard formats or combinations of values, fine-tuning the model with annotations is generally more effective than relying solely on complex prompt engineering.
D) Documents with a page count exceeding 125 pages or a file size greater than 50 MB will be processed, but with a potential reduction in extraction accuracy.
E) To improve model training, documents uploaded should represent a real use case, and the dataset should consist of diverse documents in terms of both layout and data.

3. A data scientist, 'Dl DEV', has been granted the

Despite these grants, 'DI_DEV' still receives a 'permission denied' error when attempting to ''prepare'' the Document AI model build in Snowsight. Which 'single missing privilege' is the most likely direct cause of this specific error for Document AI model build preparation?

A)

B)

C)

D)

E)

4. A development team is preparing to deploy a new Retrieval-Augmented Generation (RAG) application written in Python. They intend to use Snowflake AI Observability with the TruLens SDK to capture detailed logs and traces for debugging and performance analysis. Which of the following configurations are essential prerequisites for enabling this logging capability effectively?

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

5. An ML engineer is deploying a custom PyTorch-based image classification model, obtained from Hugging Face, to Snowpark Container Services (SPCS). The deployment requires GPU acceleration on a compute pool named 'my_gpu_pool' and specific Python packages ('torch', 'transformerS, 'opencv-python'). The scenario dictates that 'opencv-python' is only available via PyPI, while 'torch' and 'transformers' can be sourced from either conda-forge or PyPI. The engineer uses the Snowflake Model Registry to log the model. Which of the following 'log model' and 'create_service' configurations correctly specify the necessary Python dependencies and GPU utilization for this inference service, adhering to Snowflake's recommendations?

A)

B)

C)

D)

E)