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Snowflake GES-C01 Exam Syllabus Topics:

SectionWeightObjectives
Generative AI Fundamentals and Concepts20-25%- Prompt engineering principles
- LLM fundamentals and architectures
- Retrieval-Augmented Generation (RAG) concepts
- Vector embeddings and similarity search
- Fine-tuning vs. retrieval approaches
Architecture and Best Practices10-15%- LLM pipeline architecture design
- Cost management strategies
- Performance optimization techniques
- Security and privacy considerations
- Monitoring and evaluation frameworks
Snowflake Cortex AI Capabilities25-30%- Secure data handling in AI workflows
- Snowflake Copilot integration
- COMPLETE function usage and parameters
- Model selection and cost optimization
- Cortex AI functions and features
Data Preparation for Gen AI15-20%- Document processing and chunking strategies
- Unstructured data handling
- Data governance for AI workloads
- Vector stores and embeddings in Snowflake
Cortex Analyst and Semantic Layer20-25%- Business logic implementation in semantic models
- Performance tuning for analytical queries
- Text-to-SQL translation and optimization
- Semantic model design and configuration

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. An administrator is reviewing their Snowflake bill and observes higher than expected storage and cloud services compute costs for a newly deployed Cortex Search Service. They need to investigate these charges. Which of the following statements correctly explains how these specific costs are incurred or can be monitored for a Cortex Search Service?

A) High cloud services compute costs for Cortex Search are primarily driven by the complexity of the embedding model selected and can be optimized by choosing a simpler model.
B) The 'CORTEX_DOCUMENT_PROCESSING_USAGE_HISTORY view is the most appropriate tool to monitor Cortex Search storage and cloud services compute costs, as it tracks all ' Services usage.
C) The 'CORTEX_SEARCH_DAILY_USAGE_HISTORY view provides detailed breakdowns of storage costs per TB and cloud services compute credits incurred, including the 10% daily warehouse cost adjustment.
D) Storage costs are incurred for both the materialized source query data and the search index data structures, and these costs can be estimated by materializing the source query into a table using the CORTEX_SEARCH_DATA_SCAN table function, and then examining the size of that table.
E) Cloud Services compute costs for Cortex Search are always billed without any adjustments, regardless of the daily virtual warehouse compute costs, because they are considered serverless features.


2. A security engineer is developing an application that uses the Snowflake Cortex REST API to interact with LLMs, specifically to obtain structured outputs for text classification and to ensure secure communication. They are focusing on the /api/v2/cortex/ inference : complete endpoint. Which of the following statements correctly describe aspects of this interaction?

A) To strictly enforce a JSON schema for the LLM's response, the response_format parameter must be included in the request body, supplied as a JSON schema object, which helps reduce post-processing efforts.
B) Authentication for Cortex REST API requests is primarily handled through an Authorization: Bearer header, where the token can be a JSON Web Token (JWT), OAuth token, or programmatic access token.
C) To ensure the most consistent and deterministic structured output from the LLM, it is recommended to set the temperature option to a higher value, such as 0.7 or 1.0, in the request payload.
D) The Cortex REST API for LLM inference always returns the complete LLM response as a single, fully-formed JSON object once generation is finished, regardless of any streaming options.
E) For models like OpenAI (GPT) used via the Cortex REST API with structured output, the JSON schema in the response_format field must include "additionalProperties": false and a "required" field listing all properties at every node.


3. A Gen AI Specialist is tasked with enhancing a Cortex Analyst semantic model to improve the accuracy of literal string searches for product names within user queries. The product names are stored in a high-cardinality PRODUCT_NAME column in the underlying PRODUCT table. The current semantic model already defines a dimension for PRODUCT_NAME. Which of the following configurations and considerations are essential for integrating Cortex Search with Cortex Analyst to achieve this goal?

A) Option E
B) Option A
C) Option B
D) Option D
E) Option C


4. A financial analyst wants to build a generative AI application in Snowflake that can answer complex queries by integrating financial reports (unstructured data in stages) and transaction records (structured data in tables). They decide to use Snowflake Cortex Agents. Which of the following statements accurately describe the capabilities and operational aspects of Cortex Agents in this scenario?
(Select all that apply)

A) The primary compute cost for Cortex Agents is based on the number of tokens processed during the planning and reflection phases, with an additional per- message charge for each tool invocation.
B) When a user asks an ambiguous question, Cortex Agents utilise an 'Explore options' component to consider different permutations and disambiguate the query for improved accuracy.
C) Cortex Agents are designed to orchestrate tasks by planning steps, utilising tools like Cortex Analyst for structured data and Cortex Search for unstructured data, and generating comprehensive responses.
D) To provide the Agent with custom logic for specific data transformations not covered by standard tools, stored procedures or user-defined functions (UDFs) can be implemented as custom tools.
E) For monitoring agent interactions and performance on the client application, the TruLens Python packages (


5. 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) The primary cost driver for Cortex Search is the number of search queries executed against the service, with the volume of indexed data (GB/month) having a minimal impact on overall billing.
B) For optimal performance and cost efficiency, Snowflake recommends using a dedicated warehouse of size no larger than MEDIUM for each Cortex Search Service.
C) CHANGE_TRACKING
D) For embedding text, selecting a model like

E) The


Solutions:

Question # 1
Answer: D
Question # 2
Answer: A,B,E
Question # 3
Answer: C
Question # 4
Answer: B,C,D,E
Question # 5
Answer: B,C,D,E

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