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Snowflake GES-C01 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Snowflake AI & Cortex | - Snowflake Cortex capabilities - AI functions and services in Snowflake |
| Topic 2: Embeddings, Vector Search & RAG | - Retrieval-Augmented Generation (RAG) workflows - Embeddings fundamentals - Vector search in Snowflake ecosystem |
| Topic 3: Data Governance & Security | - Data privacy and access controls - Responsible use of AI in enterprise environments |
| Topic 4: Prompt Engineering | - Prompt design techniques - Optimization of prompts for LLM outputs |
| Topic 5: Generative AI Fundamentals | - Model capabilities and limitations - Core concepts of generative AI and LLMs |
| Topic 6: Model Evaluation & Responsible AI | - Evaluation metrics for LLM outputs - Bias, fairness, and explainability considerations |
| Topic 7: Use Cases & Solution Design | - End-to-end GenAI solution architecture - Enterprise AI application patterns in Snowflake |
Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:
1. A data platform architect is integrating 'SNOWFLAKE.CORTEX.EMBED TEXT 768' into a complex data pipeline for a new search application. The pipeline involves extracting text from various sources, generating embeddings, storing them in Snowflake, and performing semantic searches. Which of the following statements accurately describes a compatibility aspect or limitation when working with 'EMBED TEXT 768' and the resulting 'VECTOR' data type within Snowflake?
A) When is invoked within a Snowpark Python User-Defined Function (UDF) on Snowflake data, the data remains within Snowflake's network boundary during the embedding generation process.
B) To support diverse embedding dimensions from different models, the 'VECTOR data type can be stored efficiently within a 'VARIANT column, which automatically handles schema variations.
C) The function can be directly integrated into a dynamic table's 'SELECT statement to provide continuous, automated embedding updates for new data.
D) The 'VECTOR' data type, which stores the output of is fully compatible with all Snowflake features, including being used as a primary key in hybrid tables for fast lookups.
E) If the function is not natively available in the account's primary Snowflake region, cross-region inference cannot be enabled, thus preventing its use.
2. A data engineer is establishing a new Snowflake environment to support Document AI for processing incoming vendor invoices. They are setting up the necessary virtual warehouse, database, schema, and stages. Which of the following statements correctly identify essential considerations or requirements for this initial setup?
A) Any internal stages used for storing documents that will be processed by Document AI must explicitly enable SNOWFLAKE_SSE encryption.
B) A dedicated, smaller warehouse (e.g., 'X-SMALL', 'SMALL', or 'MEDIUM') should be created for Document AI to facilitate precise cost tracking, as scaling up warehouse size does not enhance Document AI query performance.
C) All documents intended for a single Document AI '!PREDICT operation must be stored within the same logical directory of the specified stage.
D) Snowflake recommends using a large virtual warehouse, such as an 'L' or 'XL' size, to accommodate the intensive processing demands of Document AI and ensure high throughput.
E) The database and schema where Document AI model builds are created can be freely altered after creation, allowing for agile schema evolution.
3. Considering Snowflake's Gen AI principles for cost governance within Snowflake Cortex, an ML engineer is assessing the expenditure for an LLM fine-tuning job. Which option correctly identifies how compute costs for Cortex Fine-tuning are primarily incurred and how fine-tuned models are treated regarding usage by other customers?
A) Compute costs for fine-tuning are based on the number of tokens used in training, calculated as 'number of input tokens number of epochs trained'. Fine-tuned models built using a customer's data are available exclusively for that customer's use.
B) Fine-tuning costs are a flat monthly fee, irrespective of token usage or model size. Fine-tuned models become part of Snowflake's proprietary models after training.
C) Only inference using fine-tuned models incurs costs, not the training itself. Fine-tuned models can be openly shared on the Snowflake Marketplace.
D) Costs are based on the number of fine-tuning jobs created, not tokens. Fine-tuned models are shared across all Snowflake customers to improve the general service.
E) Costs are incurred per hour of compute pool usage, similar to virtual warehouses. Fine-tuned models are anonymized and used to train future foundation models for all customers.
4. A global marketing team uses Snowflake to manage customer feedback in various languages. They need to translate customer reviews from German ("de") into English ("en") for analysis. The reviews are stored in a table named 'CUSTOMER REVIEWS' in a column called 'REVIEW TEXT'. Which of the following SQL statements correctly applies the 'SNOWFLAKE.CORTEX.TRANSLATE function and what is the expected return type for the translated text?
A) The query
B) The query
C) The query
D) The query
E) The query
5. A data engineer is reviewing the purpose of AI Observability's tracing feature within Snowflake Cortex. Which of the following statements accurately describe the benefits or functionality of tracing in this context?
A) Tracing primarily focuses on aggregating performance metrics like accuracy and latency across multiple application runs for comparison, rather than step-by-step execution details.
B) Tracing requires manual logging of each application step through custom SQL INSERT statements into an event table.
C) It allows debugging individual records by showing every step of application executions, including input prompts, retrieved context, and tool use.
D) Tracing records only the final output of an LLM inference call, not the intermediate steps.
E) It enables refinement of the application for improved accuracy, latency, and cost.
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: A,B,C | Question # 3 Answer: A | Question # 4 Answer: C | Question # 5 Answer: C,E |
