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Snowflake GES-C01 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Architecture and Best Practices | 10-15% | - Performance optimization techniques - LLM pipeline architecture design - Monitoring and evaluation frameworks - Security and privacy considerations - Cost management strategies |
| Data Preparation for Gen AI | 15-20% | - Unstructured data handling - Vector stores and embeddings in Snowflake - Data governance for AI workloads - Document processing and chunking strategies |
| Generative AI Fundamentals and Concepts | 20-25% | - Prompt engineering principles - Retrieval-Augmented Generation (RAG) concepts - Vector embeddings and similarity search - Fine-tuning vs. retrieval approaches - LLM fundamentals and architectures |
| Snowflake Cortex AI Capabilities | 25-30% | - Snowflake Copilot integration - Secure data handling in AI workflows - COMPLETE function usage and parameters - Model selection and cost optimization - Cortex AI functions and features |
| Cortex Analyst and Semantic Layer | 20-25% | - Semantic model design and configuration - Performance tuning for analytical queries - Business logic implementation in semantic models - Text-to-SQL translation and optimization |
Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:
A machine learning engineering team is evaluating two different configurations of a Retrieval Augmented Generation (RAG) application. uses for generation, while uses 'mistral-7b' with a refined prompt for the same task. They aim to compare the and 'groundedness' of the generated responses, as well as the efficiency of context retrieval. Which of the following steps are crucial for setting up AI Observability in Snowflake to facilitate a meaningful side-by-side comparison and assess these specific metrics?
- A. Option B
- B. Option E
- C. Option D
- D. Option C
- E. Option A
Correct Answer: C,D,E 🗳️
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A company is building an enterprise search solution in Snowflake, where user queries are converted into embeddings and then used to find relevant documents from a large corpus. The search logic heavily relies on VECTOR_COSINE_SIMILARITY Which of the following design choices or operational considerations are critical for a robust and efficient implementation using Snowflake's vector capabilities? (Select all that apply)
- A. To keep document embeddings updated efficiently, a
- B. When deploying custom embedding models or complex search logic, Snowpark Container Services can host GPU-accelerated environments, while
- C. Storing document embeddings in a
- D. For improved retrieval quality in RAG scenarios, it is recommended to split text into smaller chunks, ideally no more than 512 tokens, before generating embeddings for subsequent
- E. Bind variables can be used to pass query vector literals securely and efficiently to
Correct Answer: B,D 🗳️
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A financial analytics team is using to extract specific financial metrics (e.g., revenue, profit margin) from quarterly reports and requires the output in a strict JSON format for automated ingestion into a data warehouse. They've encountered issues where the LLM sometimes generates malformed JSON or includes extraneous text. Which of the following approaches will help ensure deterministic, schema-compliant JSON outputs and mitigate these 'hallucinations' related to format?
- A. Option B
- B. Option E
- C. Option D
- D. Option C
- E. Option A
Correct Answer: A,C,D,E 🗳️
Explanation: Only visible for Dumpexams members. You can sign-up / login (it's free).
An enterprise is deploying a new RAG application using Snowflake Cortex Search on a large dataset of customer support tickets. The operations team is concerned about managing compute costs and ensuring efficient index refreshes for the Cortex Search Service, which needs to be updated hourly. Which of the following considerations and configurations are relevant for optimizing cost and performance of the Cortex Search Service in this scenario?
- A. CHANGE_TRACKING
- B. The primary cost driver for Cortex Search is the number of search queries executed against the service, with the volume of indexed data (GBImonth) having a minimal impact on overall billing.
- C. For optimal performance and cost efficiency, Snowflake recommends using a dedicated warehouse of size no larger than MEDIUM for each Cortex Search Service.
- D. The
- E. For embedding text, selecting a model like
Correct Answer: A,C,D,E 🗳️
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A data application developer, adhering to Snowflake's Gen AI best practices for deploying LLMs, needs to perform inference with a newly fine-tuned llama3.1-70b model via AI_COMPLETE and expects a structured JSON output. Which of the following statements accurately describe how to configure this inference and potential limitations within Snowflake Cortex?
- A. Option B
- B. Option E
- C. Option D
- D. Option C
- E. Option A
Correct Answer: A,C 🗳️
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