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Snowflake GES-C01 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Generative AI Fundamentals and Concepts | 20-25% | - Vector embeddings and similarity search - LLM fundamentals and architectures - Retrieval-Augmented Generation (RAG) concepts - Fine-tuning vs. retrieval approaches - Prompt engineering principles |
| Snowflake Cortex AI Capabilities | 25-30% | - Cortex AI functions and features - COMPLETE function usage and parameters - Snowflake Copilot integration - Secure data handling in AI workflows - Model selection and cost optimization |
| Data Preparation for Gen AI | 15-20% | - Data governance for AI workloads - Unstructured data handling - Vector stores and embeddings in Snowflake - Document processing and chunking strategies |
| Architecture and Best Practices | 10-15% | - Cost management strategies - LLM pipeline architecture design - Performance optimization techniques - Security and privacy considerations - Monitoring and evaluation frameworks |
| Cortex Analyst and Semantic Layer | 20-25% | - Semantic model design and configuration - Text-to-SQL translation and optimization - Business logic implementation in semantic models - Performance tuning for analytical queries |
Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:
A company wants to ingest and process scanned invoices and digitally-born contracts in Snowflake. They need to extract all text, preserving layout for contracts and just the text content for scanned invoices. Which AI_PARSE_DOCUMENT modes would be most appropriate for this scenario, and what is the primary purpose of the function itself?
- A. Primary purpose is to summarize text. For contracts, use OCR mode; for invoices, use LAYOUT mode.
- B. Primary purpose is to generate new text. For contracts, use OCR mode; for invoices, use LAYOUT mode.
- C. Primary purpose is to translate text. Both document types should use LAYOUT mode.
- D. Primary purpose is to extract data and layout. For contracts, use LAYOUT mode; for invoices, use OCR mode.
- E. Primary purpose is to classify text. For contracts, use LAYOUT mode; for invoices, use OCR mode.
Correct Answer: D 🗳️
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An ML engineer is working on a Snowflake project that requires storing and comparing high-dimensional feature vectors extracted from customer interaction logs. They need to ensure the vector data type is correctly defined and then perform an inner product calculation.
Which of the following statements about defining and using the
VECTOR
data type and
VECTOR_INNER_PRODUCT
function in Snowflake SQL are correct? (Select all that apply)
- A. Direct comparison operators like
- B. To store a vector with 512 floating-point dimensions in a table, the correct SQL syntax for adding a column would be:
- C. When calling
- D. The
- E. The
Correct Answer: B,C,D 🗳️
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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.
Correct Answer: B,C,E 🗳️
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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.
Correct Answer: A,D 🗳️
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A Gen AI developer has a Document AI pipeline that uses a query with 'GET PRESIGNED URL' to process multi-page PDF documents. Despite the internal stage being correctly set up with 'SNOWFLAKE SSE' encryption and the model build being published, they observe inconsistent results. Some documents result in a Received HTTP 403 response for presigned URL. URL may be expired.
error, while other documents (containing complex diagrams and dense text in an unsupported language like Korean) are processed, but the extracted information is often incomplete or inaccurate.
Which two factors are most likely contributing to these observed issues?
- A. The documents are in an unsupported language or exceed the maximum page length of 125 pages, causing extraction failures or inaccuracies.
- B. The default expiration time for the 'GET PRESIGNED URL' function is causing some URLs to expire before the Document AI model can process them.
- C. The role lacks the 'EXECUTE TASK' privilege, preventing the scheduled pipeline tasks from running.
- D. The '!PREDICT method is being called with an outdated model build version instead of the latest one, leading to performance degradation.
- E. The Document AI model is returning answers longer than its limit of 512 tokens for entity extraction or 2048 tokens for table extraction.
Correct Answer: A,B 🗳️
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