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Snowflake GES-C01 Exam Syllabus Topics:

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

Question 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. 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.
B. 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.
C. The

D. 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

E. Cortex Search automatically handles text chunking and embedding generation for the source data, eliminating the need for manual ETL processes for these steps.

Question 2

A data application developer is tasked with building a multi-turn conversational AI application using Streamlit in Snowflake (SiS) that leverages the COMPLETE (SNOWFLAKE. CORTEX) LLM function. To ensure the conversation flows naturally and the LLM maintains context from previous interactions, which of the following is the most appropriate method for handling and passing the conversation history?

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

Question 3

A business team using a Snowflake Cortex Analyst-powered chatbot reports that follow-up questions in multi-turn conversations are sometimes slow to process, impacting user experience. The development team wants to optimize for responsiveness while maintaining accuracy in SQL generation. Which of the following strategies directly addresses latency in multi-turn conversations within Cortex Analyst, considering its underlying mechanisms?

A. Switch the underlying text-to-SQL LLM to a smaller model, such as
B. Implement an explicit LLM summarization agent within the semantic model to condense conversation history before it's passed to subsequent LLM calls.
C. Configure the semantic model to reset the conversation context after every three turns to limit token count.
D. Increase the warehouse size used for Cortex Analyst queries to 'Large' to accelerate LLM inference.
E. Rely on

Question 4

An organization operating in the AWS US West 2 (Oregon) region needs to process sensitive customer support tickets using Snowflake Cortex LLM functions. Due to the diverse availability of specific LLMs, they are considering enabling CORTEX_ENABLED_CROSS_REGION. What is a key data safety and security consideration when enabling CORTEX_ENABLED_CROSS_REGION for Snowflake Cortex LLM functions, specifically regarding data storage and persistence?

A. User inputs and service-generated prompts will be stored in a cache in the remote region to optimize subsequent requests.
B. Data transmitted across regions for inference is encrypted by default, but the encryption keys are managed by the third-party cloud provider in the remote region.
C.

D. It may lead to increased compute costs if the cross-region model is more expensive, but data movement guarantees remain unchanged.
E. It enables inference for features not supported in the local region by allowing data to be processed in a different Snowflake region, and user inputs, prompts, and outputs are not stored or cached.

Question 5

A Gen AI specialist is preparing to upload a large volume of diverse documents to an internal stage for Document AI processing. The objective is to extract detailed information, including lists of items and potentially classifying document types, and then automate this process. Which of the following statements represent 'best practices or important considerations/limitations' when preparing documents and setting up the Document AI workflow in Snowflake? (Select ALL that apply.)

A. If the Document AI model does not find an answer for a specific field, the '!PREDICT method will omit the 'value' key but will still return a 'score' key to indicate confidence that the answer is not present.
B. For continuous processing of new documents, it is best practice to create a stream on the internal stage and a task to automate the '!PREDICT method execution.
C. When defining data values for extraction, especially for nonstandard formats or combinations of values, fine-tuning the model with annotations is generally more effective than relying solely on complex prompt engineering.
D. Documents with a page count exceeding 125 pages or a file size greater than 50 MB will be processed, but with a potential reduction in extraction accuracy.
E. To improve model training, documents uploaded should represent a real use case, and the dataset should consist of diverse documents in terms of both layout and data.

Solutions: