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Snowflake GES-C01 Exam Syllabus Topics:

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. 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) 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.
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) 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.
D) 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.
E) 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.

2. A data engineering team aims to automatically classify incoming customer support requests into predefined categories ('Technical Issue', 'Billing Inquiry', 'General Question') as part of their Snowflake data ingestion pipeline. The goal is to achieve high classification accuracy while managing LLM inference costs efficiently. Which of the following strategies, when applied within a Snowflake data pipeline using Streams and Tasks, would best contribute to meeting these objectives?

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

3. A Gen AI Specialist is developing a conversational analytics application using Cortex Analyst, aiming to provide a seamless multi-turn conversation experience for business users querying structured dat a. The team observes that follow-up questions are sometimes misinterpreted, especially when the conversation history is long. Which of the following statements accurately describe how Cortex Analyst handles multi-turn conversations and key considerations for optimizing this functionality?

A) Cortex Analyst incorporates an additional LLM summarization agent before its original workflow to rewrite current-turn questions based on conversation history, with Llama 3.1 70B being a recommended model for this task due to its performance in evaluating summarization quality.
B) To address misinterpretation in long conversations, the max_tokens parameter for the Cortex Analyst REST API should be significantly increased to ensure the LLM receives the complete historical context without truncation.
C) When a user shifts intent frequently in a multi-turn conversation, Cortex Analyst automatically resets the conversation history to prevent misinterpretations and improve accuracy.
D) Multi-turn conversation in Cortex Analyst is primarily handled by the CORTEX_ANALYST_MODEL AZURE_OPENAI parameter, which, when enabled, allows Azure OpenAl models to manage conversational context more effectively.
E) Cortex Analyst simply passes the entire conversation history to all subsequent LLM calls, and optimizing this requires manually truncating the array in messages the REST API request.

4. A data architect is designing a workflow to programmatically extract highly structured data from various text inputs using Snowflake Cortex AI's AI_COPIPLETE function through its REST API. They require the output to strictly adhere to a complex JSON schema for downstream processing and need to manage associated costs. Which of the following statements accurately describe aspects of this approach?

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

5. A large e-commerce company plans to implement real-time sentiment analysis on millions of incoming customer reviews using SNOWFLAKE. CORTEX. SENTIMENT. They are concerned about managing costs and ensuring efficient processing. Which of the following statements about cost considerations and performance optimizations for SNOWFLAKE. CORTEX. SENTIMENT are true?
(Select all that apply)

A) Billing for SNOWFLAKE. CORTEX. SENTIMENT is primarily based on the number of output tokens generated in the response.
B) Snowflake recommends using a smaller warehouse (no larger than MEDIUM), as larger warehouses do not increase performance for SENTIMENT function calls.
C) The actual number of tokens processed and billed for a SENTIMENT call is typically higher than the raw input text length, due to an internal prompt added by the function.
D) The fixed billing rate for the SENTIMENT function is 0.08 Credits per one million input tokens processed.
E) The newer AI_SENTIMENT function is a free, serverless alternative to SNOWFLAKE. CORTEX. SENTIMENT, offering cost savings for high-volume scenarios.

Solutions: