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Snowflake GES-C01 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Architecture and Best Practices | 10-15% | - Performance optimization techniques - Cost management strategies - Monitoring and evaluation frameworks - LLM pipeline architecture design - Security and privacy considerations |
| Data Preparation for Gen AI | 15-20% | - Vector stores and embeddings in Snowflake - Unstructured data handling - Data governance for AI workloads - Document processing and chunking strategies |
| Cortex Analyst and Semantic Layer | 20-25% | - Performance tuning for analytical queries - Semantic model design and configuration - Business logic implementation in semantic models - Text-to-SQL translation and optimization |
| 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 |
| Generative AI Fundamentals and Concepts | 20-25% | - Fine-tuning vs. retrieval approaches - Vector embeddings and similarity search - LLM fundamentals and architectures - Retrieval-Augmented Generation (RAG) concepts - Prompt engineering principles |
Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:
A machine learning engineer needs to fine-tune the 'mistral-7b' LLM using Snowflake Cortex for a specialized task. They have prepared training data in a Snowflake table. Which of the following statements correctly describe the process, requirements, and cost considerations for initiating this fine-tuning job?
- A. The fine-tuned model will automatically appear in the Snowflake Model Registry and is available for sharing with other accounts using Data Sharing, even if it contains user code.
- B. The training data query result must explicitly include columns named
- C. The 'max_epochs' option in the 'options' JSON object can be set to any positive integer to control the training duration, but it is capped at a maximum of 50 epochs.
- D. To create the fine-tuning job, the engineer should use the SQL command:
- E. The cost for fine-tuning is incurred based on the number of tokens used in training, calculated as:
Correct Answer: B,D,E 🗳️
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- A.
- B.
- C.
- D.
- E.
Correct Answer: A,B,C,D,E 🗳️
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A data team has implemented a Snowflake data pipeline using SQL tasks that process customer call transcripts daily. This pipeline relies heavily on SNOWFLAKE. CORTEX. COMPLETE() (or its updated alias) for various text analysis tasks, such as sentiment analysis and summary generation. Over time, they observe that the pipeline occasionally fails due to LLM-related errors, and the compute costs are higher than anticipated. What actions should the team take to improve the robustness and cost-efficiency of this data pipeline? (Select all that apply.)
- A. Option D
- B. Option A
- C. Option C
- D. Option B
- E. Option E
Correct Answer: A,B,C 🗳️
Explanation: Only visible for GuideTorrent members. You can sign-up / login (it's free).
A data engineer is designing a new feature for a Retrieval Augmented Generation (RAG)-based application in Snowflake. They plan to store document embeddings and perform semantic similarity searches to retrieve relevant context for an LLM. Which of the following statements about using the VECTOR data type and related functions in Snowflake are true? (Select all that apply.)
- A. Option D
- B. Option A
- C. Option C
- D. Option B
- E. Option E
Correct Answer: B,C,E 🗳️
Explanation: Only visible for GuideTorrent members. You can sign-up / login (it's free).
A SnowPro-certified engineer is tasked with setting up AI Observability for a new generative AI application built using Snowpark Python. The application relies on external Python libraries (e.g., TruLens SDK components) and will process sensitive financial documents. Which of the following steps are crucial for the successful setup and secure operation of AI Observability within Snowflake, given these considerations?
- A. Option D
- B. Option A
- C. Option C
- D. Option B
- E. Option E
Correct Answer: B,C,D 🗳️
