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Snowflake GES-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Snowflake Cortex AI Capabilities	25-30%	- Snowflake Copilot integration - Model selection and cost optimization - Cortex AI functions and features - COMPLETE function usage and parameters - Secure data handling in AI workflows
Data Preparation for Gen AI	15-20%	- Unstructured data handling - Vector stores and embeddings in Snowflake - Document processing and chunking strategies - Data governance for AI workloads
Generative AI Fundamentals and Concepts	20-25%	- Prompt engineering principles - LLM fundamentals and architectures - Retrieval-Augmented Generation (RAG) concepts - Fine-tuning vs. retrieval approaches - Vector embeddings and similarity search
Cortex Analyst and Semantic Layer	20-25%	- Semantic model design and configuration - Business logic implementation in semantic models - Performance tuning for analytical queries - Text-to-SQL translation and optimization
Architecture and Best Practices	10-15%	- Performance optimization techniques - Cost management strategies - Monitoring and evaluation frameworks - Security and privacy considerations - LLM pipeline architecture design

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

Question 1

A machine learning engineer is building a product recommendation system in Snowflake that uses item embeddings and customer query embeddings for similarity matching. They plan to use the VECTOR_L1_DISTANCE function to find the closest products to a user's query. Which statement accurately describes the cost and data type considerations for this approach?

A. The
B. Executing the

C. Storing the item and query embeddings in
D. The maximum dimension for a
E. Both

Question 2

A Data Application Developer is building a Streamlit chat application powered by Snowflake Cortex Analyst. Users frequently ask questions involving specific product names, such as "What was the total sales of 'Luxury Coffee Beans' last quarter?". The semantic model has a product_name dimension with high cardinality. The developer wants to ensure Cortex Analyst accurately identifies these specific product literals in user queries. Given this scenario, which of the following approaches should the developer consider to optimize literal search capabilities and enhance Cortex Analyst responses?

A. Option C
B. Option E
C. Option B
D. Option A
E. Option D

Question 3

A data engineering team needs to configure their Snowflake environment to process documents using AI_PARSE_DOCUMENT and generate text embeddings using EMBED_TEXT_1024 with the voyage-multilingual-2 model. Their Snowflake account is in a region where these specific capabilities or models are only available via cross-region inference. The team needs to ensure these functions work correctly without constant region-specific model selection. Which of the following is the correct configuration action and an important consideration?

A. Option C
B. Option E
C. Option B
D. Option A
E. Option D

Question 4

A financial institution is building a Document AI model to process loan applications. They need to extract all applicant names (which can be multiple, forming a list), classify the application as 'Approved' or 'Rejected' based on various internal criteria (not explicitly stated in the document), and normalize diverse date formats (e.g., '1st Jan 2023', '01/01/2023') to 'YYYY-MM-DD'. Which of the following approaches represent 'best practices' for defining data values and training the Document AI model for these complex extractions? (Select ALL that apply.)

A. When defining data values for combinations of values, arrays, or nonstandard formats, fine-tuning the model with annotations that 'show' the expected result is generally more effective than 'telling' it via complex prompt engineering.
B. For extracting lists of items from tables, use individual data values for each column and then merge these lists in the pipeline, ensuring adequate training with sample data, including NULL values and correct order, for reconstruction.
C. To classify applications as 'Approved' or 'Rejected', define a data value like 'What is the application status?' and ensure the training dataset includes annotated examples for both 'Approved' and 'Rejected' classes.
D. For applicant names, define a single value 'applicant_name' and rely solely on advanced prompt engineering to instruct the model on how to identify and list all names, without providing specific examples through annotations.
E. To normalize diverse date formats to 'YYYY-MM-DD', train the model with sufficient annotations, showing the desired output format for various input date formats, allowing Document AI to learn the normalization internally.

Question 5

A data engineer is constructing a Retrieval Augmented Generation (RAG) pipeline in Snowflake to allow users to query a large corpus of unstructured customer support transcripts using natural language. The goal is to retrieve relevant transcript snippets and then use a Large Language Model (LLM) to generate an answer. Which sequence of steps and Snowflake components would effectively implement this RAG pipeline?