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Snowflake GES-C01 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Architecture and Best Practices | 10-15% | - Performance optimization techniques - Monitoring and evaluation frameworks - LLM pipeline architecture design - Cost management strategies - Security and privacy considerations |
| Topic 2: Cortex Analyst and Semantic Layer | 20-25% | - Text-to-SQL translation and optimization - Business logic implementation in semantic models - Performance tuning for analytical queries - Semantic model design and configuration |
| Topic 3: Snowflake Cortex AI Capabilities | 25-30% | - Secure data handling in AI workflows - Snowflake Copilot integration - Model selection and cost optimization - Cortex AI functions and features - COMPLETE function usage and parameters |
| Topic 4: Generative AI Fundamentals and Concepts | 20-25% | - LLM fundamentals and architectures - Prompt engineering principles - Fine-tuning vs. retrieval approaches - Retrieval-Augmented Generation (RAG) concepts - Vector embeddings and similarity search |
| Topic 5: Data Preparation for Gen AI | 15-20% | - Unstructured data handling - Document processing and chunking strategies - Vector stores and embeddings in Snowflake - Data governance for AI workloads |
Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:
A financial institution needs to process thousands of incoming PDF loan application forms daily, extracting applicant names, loan amounts, and submission dates, and loading them into a Snowflake table. They aim for continuous processing with minimal manual intervention. Which of the following statements correctly describe how Document AI can be used in an automated SQL pipeline for this purpose?
- A. The pipeline can leverage the <model build name> ! PREDICT method within a CREATE TASK statement to automatically process new PDFs as they arrive in an internal or external stage, once the Document AI model build is published.
- B. The SNOWFLAKE .DOCUMENT_INTELLIGENCE_CREATOR database role alone is sufficient for defining the model build and configuring the processing pipeline, without needing additional CREATE MODEL privileges on the schema.
- C. Document AI's PREDICT method natively supports all PDF files up to 500 MB and 500 pages, allowing for large-scale, single-query processing without requiring users to split documents into smaller chunks.
- D. The extracted information, including confidence scores and values, is returned as a JSON object, which can then be parsed into separate columns in a Snowflake table using SQL functions like LATERAL FLATTEN.
- E. To ensure continuous data ingestion and processing, a STREAM can be created on the stage to detect new PDF documents, triggering the TASK for extraction and subsequent loading into a Snowflake table.
Correct Answer: A,D,E 🗳️
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A development team is preparing to deploy a new Retrieval-Augmented Generation (RAG) application written in Python. They intend to use Snowflake AI Observability 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 E
- C. Option A
- D. Option D
- E. Option B
Correct Answer: A,B,C,E 🗳️
Explanation: Only visible for DumpsMaterials members. You can sign-up / login (it's free).
A company is developing a RAG application to provide concise and highly relevant answers to user queries from a vast knowledge base of technical documents. They are using Cortex Search for retrieval and are considering different embedding models and text chunking strategies to optimise the system. Which of the following statements about Cortex Search embedding models and RAG best practices are correct? (Select all that apply)
- A. For optimal RAG retrieval quality with Cortex Search, it is recommended to split text into chunks of no more than 512 tokens, even when using models with larger context windows like 'snowflake-arctic-embed-l-v2.0-8k'.
- B. The 'voyage-multilingual-2 model is suitable for multilingual documents and has a significantly larger context window (32000 tokens) compared to 'snowflake- arctic-embed-l-v2.C (512 tokens), making it more robust for longer text inputs.
- C. The
- D. The cost for embedding models in Cortex Search, such as 'snowflake-arctic-embed-l-v2.0' and 'e5-base-v2, is incurred based on both input and output tokens.
- E. Using the 'snowflake-arctic-embed-l-v2.0-8k' model, which has an 8192-token context window, allows processing entire large technical documents as a single chunk for embedding, leading to better RAG results by preserving full document context.
Correct Answer: A,B,C 🗳️
Explanation: Only visible for DumpsMaterials members. You can sign-up / login (it's free).
An organization is implementing a two-tier LLM access control strategy in Snowflake. First, common models like 'mistral-7b' and 'llama3.1-8b' need to be broadly accessible to all users granted the 'SNOWFLAKE-CORTEX USER database role. Second, a specialized data science team, using the ANALYST ROLE', requires exclusive access to the higher-capability 'claude-3-5- sonnet' model, which should NOT be generally available through the broad access mechanism. Which set of SQL commands, executed by the 'ACCOUNTADMIN" role, correctly establishes this access control strategy?
- A.
- B.
- C.
- D.
- E.
Correct Answer: D 🗳️
Explanation: Only visible for DumpsMaterials members. You can sign-up / login (it's free).
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 E
- C. Option A
- D. Option D
- E. Option B
Correct Answer: A,B,C,E 🗳️
Explanation: Only visible for DumpsMaterials members. You can sign-up / login (it's free).
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