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Snowflake GES-C01 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Cortex Analyst and Semantic Layer | 20-25% | - Semantic model design and configuration - Text-to-SQL translation and optimization - Performance tuning for analytical queries - Business logic implementation in semantic models |
| Architecture and Best Practices | 10-15% | - Cost management strategies - Performance optimization techniques - Monitoring and evaluation frameworks - LLM pipeline architecture design - Security and privacy considerations |
| Snowflake Cortex AI Capabilities | 25-30% | - COMPLETE function usage and parameters - Secure data handling in AI workflows - Model selection and cost optimization - Snowflake Copilot integration - Cortex AI functions and features |
| Data Preparation for Gen AI | 15-20% | - Document processing and chunking strategies - Unstructured data handling - Vector stores and embeddings in Snowflake - Data governance for AI workloads |
| Generative AI Fundamentals and Concepts | 20-25% | - Fine-tuning vs. retrieval approaches - Prompt engineering principles - Retrieval-Augmented Generation (RAG) concepts - LLM fundamentals and architectures - Vector embeddings and similarity search |
Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:
Question 1
A business intelligence team wants to enable non-technical users to query their Snowflake data using natural language for sales analytics reports via Cortex Analyst. They are designing the YAML semantic model. Which of the following statements accurately describe key aspects of designing and utilizing a semantic model for Cortex Analyst?
A. The base_table field in a logical table definition must directly reference a physical table and cannot point to a view, as Cortex Analyst only works with raw tables for performance reasons.
B. To optimize performance, Snowflake recommends including all available tables and columns from the underlying database in a semantic model, especially for complex analytical tasks.
C. The VARIANT, OBJECT, GEOGRAPHY, and ARRAY data types are fully supported for dimension and fact columns within a semantic model, offering flexibility for diverse data structures.
D. facts in a semantic model are primarily used to define categorical data, such as product types or customer segments, to support filtering operations.
E. Dimensions in the semantic model YAML, such as 'state' or 'product_category', can include synonyms to map common business terms to underlying technical column names, thereby improving natural language understanding for users.
Question 2
An ML Engineer has developed a custom PyTorch model for GPU-powered inference and successfully built an OCI-compliant image locally. They now need to push this image to a Snowflake image repository and configure a Snowpark Container Service to use it. The Snowflake account identifier is my org_name_my_account_id_prod. Which set of commands correctly demonstrates tagging the local image and pushing it to the repository?
A. Option E
B. Option A
C. Option D
D. Option C
E. Option B
Question 3
A data application developer is using the Snowflake Cortex COMPLETE function to power a multi-turn conversational AI application. They want to ensure responses are creative but not excessively long, adhere to a specific JSON structure, and are filtered for safety. Given the following SQL query snippet, which statements accurately describe the impact of the specified options?
A. For a multi-turn conversation, previous user prompts and model responses should be passed in the
B. The
C. Including a
D. The
E. Setting
Question 4
A security-conscious data scientist in an Azure East US 2 (Virginia) account wants to fine-tune a mistral -7b model for a specific text summarization task and then deploy it for real-time inference using the Cortex REST API. The mistral-7b base model is natively available for fine-tuning in Azure East US 2 (Virginia). For subsequent inference using the fine-tuned model, they need to understand the regional and cross-region inference considerations. Which of the following statements are correct?
A. Option E
B. Option A
C. Option D
D. Option C
E. Option B
Question 5
A data engineering team is building a pipeline in Snowflake that uses a SQL task to call various Snowflake Cortex LLM functions (e.g., AI_COMPLETE, AI EMBED) on large datasets of customer interaction logs. The team observes fluctuating costs and occasional query failures, which sometimes halt the pipeline. To address these issues and ensure an efficient, robust, and monitorable pipeline, which of the following actions or considerations are essential? (Select all that apply.)
A. Option E
B. Option A
C. Option D
D. Option C
E. Option B
Solutions:
| Question 1 Answer: E | Question 2 Answer: B,E | Question 3 Answer: B,C,D | Question 4 Answer: A,B,C,E | Question 5 Answer: A,B,E |
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