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Snowflake GES-C01 Exam Syllabus Topics:

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A company is planning to process a large volume of legal documents to generate summaries using SNOWFLAKE. CORTEX. SUMMARIZE. Given the scale, they are acutely focused on managing costs and optimizing performance. Which of the following statements are true regarding the cost and performance characteristics of using SNOWFLAKE. CORTEX. SUMMARIZE? (Select all that apply)

A) The SUWARIZE function is billed primarily based on the number of output tokens generated in the response, not input tokens.
B) The context window for the SWIMARIZE function is 4,096 tokens, ensuring efficiency for short documents only.
C) The fixed billing rate for the SUMMARIZE function is 0.10 Credits per one million Tokens processed.
D) Snowflake recommends using a larger warehouse (e.g., L or XL) for SUMMARIZE function calls to significantly improve processing performance for high-volume tasks.
E) For SUWARIZE, Snowflake adds an internal prompt to the user's input text, which increases the total input token count for billing purposes beyond the raw text length.

2. A marketing analyst wants to quickly gauge the overall sentiment of customer feedback stored in a Snowflake table called CUSTOMER_FEEDBACK, which has a column FEEDBACK_TEXT. They decide to use the SNOWFLAKE .CORTEX.SENTIMENT function to process a review. Consider the following SQL query for a specific review:

Which of the following correctly describes the expected output format and interpretation of the sentiment_score for the given input?

A) The output will be a JSON object containing a 'label' field, and values around 0 indicate a neutral sentiment.
B) The output will be a floating-point number between -1 and 1 (inclusive), where a value of 1 indicates strong positive sentiment and -1 indicates strong negative sentiment.
C) The output will be a string like 'Positive' or 'Negative', and a score close to 1 indicates strong positive sentiment.
D) The output will be an integer between 0 and 1 00, where higher values denote more positive sentiment.
E) The output will be a boolean value (TRUE/FALSE) indicating if the sentiment is positive, and 0.5 represents a neutral sentiment.

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 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.
B) When a user shifts intent frequently in a multi-turn conversation, Cortex Analyst automatically resets the conversation history to prevent misinterpretations and improve accuracy.
C) 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.
D) 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.
E) 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.

4. A data scientist is working on a new feature that involves querying a Cortex Search Service and integrating the results into various downstream processes. They are concerned about potential data type compatibility issues and limitations within the Snowflake environment. Which of the following statements accurately describe how to query a Cortex Search Service or the limitations of the 'VECTOR data type and Cortex Search itself? (Select all that apply)

A) Cortex Search Services can be configured as a source for Snowflake dynamic tables, enabling continuous and automated synchronization of the search index with base data.
B) Cortex Search Services can only be queried using the 'SNOWFLAKE.CORTEX.SEARCH_PREVIEW' SQL function and do not offer a programmatic interface for applications.
C) The "VECTOR data type, used to store embeddings generated for Cortex Search, is fully supported as a primary key in Snowflake's hybrid tables to accelerate similarity searches.
D) The 'VECTOR data type, which stores the output of embedding models like those used by Cortex Search, is explicitly not supported in 'VARIANT columns.
E) To retrieve the support ticket most relevant to a query about 'internet issues', filtered to return results only in the 'North America' region, one can use the 'filter' parameter in the 'SEARCH PREVIEW function's JSON argument like so:

5. A financial analytics team is using to extract specific financial metrics (e.g., revenue, profit margin) from quarterly reports and requires the output in a strict JSON format for automated ingestion into a data warehouse. They've encountered issues where the LLM sometimes generates malformed JSON or includes extraneous text. Which of the following approaches will help ensure deterministic, schema-compliant JSON outputs and mitigate these 'hallucinations' related to format?

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

Solutions: