Snowflake GES-C01 : SnowPro® Specialty: Gen AI Certification Exam

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Snowflake GES-C01 Exam Syllabus Topics:

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A data scientist is tasked with improving the accuracy of an LLM-powered chatbot that answers user questions based on internal company documents stored in Snowflake. They decide to implement a Retrieval Augmented Generation (RAG) architecture using Snowflake Cortex Search. Which of the following statements correctly describe the features and considerations when leveraging Snowflake Cortex Search for this RAG application?

A) For optimal search results with Cortex Search, source text should be pre-split into chunks of no more than 512 tokens, even when using models with larger context windows like

B) Cortex Search automatically handles text chunking and embedding generation for the source data, eliminating the need for manual ETL processes for these steps.
C) To create a Cortex Search Service, one must explicitly specify an embedding model and manually manage its underlying infrastructure, similar to deploying a custom model via Snowpark Container Services.
D) Enabling change tracking on the source table for the Cortex Search Service is optional; the service will still refresh automatically even if change tracking is disabled.
E) The

2. A Gen AI Specialist is leveraging Snowflake Document AI to extract specific entities and table data from a large and varied collection of documents. They are aware of potential limitations and want to understand the expected outcomes when processing different types of files. Considering a scenario where a Document AI model build is used with the '!PREDICT' method, which of the following statements accurately describe the expected behavior or potential issues based on Document AI's conditions and limitations?

A) A document written entirely in Ukrainian will be processed by Document AI, and the extracted information will be of satisfactory quality due to extensive multilingual support.
B) In a table extraction task, if a specific cell (e.g., 'tablellitem') is empty, the resulting JSON will omit the 'value' key for that cell, but will still provide a 'score' indicating the model's confidence that the cell is empty.
C) Processing a legal contract document that is 130 pages long will likely result in a '_processingErrors' message indicating that the document has too many pages.
D) If a question for an entity, like 'total_invoice_amount', does not find a corresponding value in a document, the JSON output for will contain a 'value' key with a 'null' string and a 'score' key indicating the model's confidence in the absence of the answer.
E) If the extracted answer to a question for a single entity (e.g., is very long, it will be automatically truncated to a maximum of 2048 tokens.

3. A data engineering team is building an automated pipeline in Snowflake to process customer reviews. They need to use AI_COMPLETE to extract specific details like product, sentiment, and issue type, and store them in a strictly defined JSON format for seamless downstream integration. They aim to maximize the accuracy of the structured output and manage potential model limitations. Which statements accurately reflect the best practices and characteristics when using AI_COMPLETE with structured outputs for this scenario?

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

4. A compliance officer is reviewing the usage of Snowflake Cortex LLM functions and the Cortex REST API within their organization, specifically focusing on the implementation and impact of Cortex Guard. They observe several instances where 'guardrails' were enabled. Which of the following statements accurately describe the behavior and cost considerations of Cortex Guard when integrated with Snowflake Cortex LLM functions or the Cortex REST API?

A) Cortex Guard is inherently part of all Cortex LLM functions and does not require explicit enablement via 'guardrails: TRUE for SQL functions or the REST API.
B) When Cortex Guard is enabled and a response is deemed unsafe, the LLM-generated output is replaced with a predefined message, and only the input tokens for Cortex Guard processing ('guard_tokens') are billed, not the potentially unsafe completion tokens.
C) Cortex Guard can be configured with a custom message using the argument in the options object for both 'COMPLETE SQL function and the Cortex REST API.
D) The underlying model for Cortex Guard is Meta's Llama Guard 3, and its processing costs are separate from the primary LLM inference costs.
E) Cortex Guard operates by evaluating responses after the LLM has fully generated its content, and it incurs additional compute cost for both input and output tokens during its processing.

5. A software development team is building a conversational AI application within Snowflake, aiming to provide a dynamic and stateful chat experience for users. The application needs to handle follow-up questions while maintaining context, provide responses with a degree of creative variation, and actively filter out any potentially harmful content. The team utilizes the SNOWFLAKE. CORTEX. COMPLETE (or AI_COMPLETE) function.

A)

B)

C)

D)

Solutions: