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Snowflake GES-C01 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Snowflake Cortex AI Capabilities | 25-30% | - Cortex AI functions and features - Snowflake Copilot integration - COMPLETE function usage and parameters - Secure data handling in AI workflows - Model selection and cost optimization |
| Topic 2: Data Preparation for Gen AI | 15-20% | - Unstructured data handling - Data governance for AI workloads - Document processing and chunking strategies - Vector stores and embeddings in Snowflake |
| Topic 3: Generative AI Fundamentals and Concepts | 20-25% | - Retrieval-Augmented Generation (RAG) concepts - Fine-tuning vs. retrieval approaches - Vector embeddings and similarity search - LLM fundamentals and architectures - Prompt engineering principles |
| Topic 4: Cortex Analyst and Semantic Layer | 20-25% | - Text-to-SQL translation and optimization - Business logic implementation in semantic models - Semantic model design and configuration - Performance tuning for analytical queries |
| Topic 5: Architecture and Best Practices | 10-15% | - Security and privacy considerations - Performance optimization techniques - Monitoring and evaluation frameworks - LLM pipeline architecture design - Cost management strategies |
Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:
1. A Snowflake data engineering team needs to implement a system where they can call the SNOWFLAKE .CORTEX. COMPLETE function using the model, which is hosted in a different region than their primary Snowflake account. They also want to ensure that only llama3.1-8b approved models are callable. Which of the following configurations or privileges are necessary to achieve these requirements?
A) Option B
B) Option C
C) Option E
D) Option D
E) Option A
2. A data engineering team is setting up a Retrieval Augmented Generation (RAG) application using Snowflake Cortex Search to provide contextual answers from customer support transcripts. The transcripts are stored in a Snowflake table named SUPPORT_TRANSCRIPTS. Which of the following statements are crucial considerations or accurate facts regarding the initial setup and configuration of the Cortex Search Service for this use case?
A) Cortex Search is designed to get users up and running quickly with a hybrid (vector and keyword) search engine on text data, handling embedding, infrastructure maintenance, and search quality parameter tuning automatically.
B) Snowflake recommends using a dedicated virtual warehouse of any size, including X-Large or 2X-Large, for each Cortex Search Service to ensure the fastest possible materialization of search indexes during creation and refresh.
C) Cortex Search Services currently support replication and cloning, allowing for easy disaster recovery and geographical distribution of the search index.
D) The CREATE CORTEX SEARCH SERVICE command requires that CHANGE_TRACKING = TRUE be enabled on the source table, especially if the role creating the service is not the table owner. This ensures that the service can track updates to the base data.
E) Columns specified in the ATTRIBUTES field during service creation are only used for filtering search results and do not need to be present in the source query.
3. An ML Engineer deploys a custom fine-tuned LLM on Snowpark Container Services (SPCS) to process multiple independent text streams, generating structured output. The team observes that some streams fail to process, leading to incomplete results, and they also want to effectively monitor the cost of their deployment. Which actions are appropriate for troubleshooting and cost management in this scenario?
A) Option B
B) Option C
C) Option E
D) Option D
E) Option A
4. An operations manager is concerned about the governance and monitoring of a newly deployed Cortex Analyst application, especially regarding cost tracking and how multi-turn conversations are handled. Which statement accurately describes a feature for monitoring or managing a Cortex Analyst application?
A) Option B
B) Option C
C) Option E
D) Option D
E) Option A
5. A new Gen AI team member attempts to use Document AI to process a batch of 1 ,500 scanned image files (JPG) that are 70 MB each, stored in an internal stage that was created without specifying an encryption type. Their '!PREDICT' queries consistently fail with various errors. Which of the following are valid reasons for the '!PREDICT' queries to fail in this scenario?
A) Processing 1 ,500 documents in one query exceeds the maximum limit for Document AI.
B) The internal stage was not created with 'ENCRYPTION = (TYPE = 'SNOWFLAKE SSE'V, which is a requirement for Document AI.
C) The team member's role lacks the database role, which is essential for using Document AI functions.
D) The individual JPG files exceed the maximum supported file size for Document AI.
E) JPG is an unsupported file format for Document AI.
Solutions:
| Question # 1 Answer: A,B,E | Question # 2 Answer: A,D | Question # 3 Answer: B,C,D | Question # 4 Answer: B | Question # 5 Answer: A,B,C,D |


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