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Snowflake GES-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Generative AI Fundamentals and Concepts	20-25%	- Prompt engineering principles - Fine-tuning vs. retrieval approaches - LLM fundamentals and architectures - Vector embeddings and similarity search - Retrieval-Augmented Generation (RAG) concepts
Snowflake Cortex AI Capabilities	25-30%	- Snowflake Copilot integration - Secure data handling in AI workflows - Cortex AI functions and features - Model selection and cost optimization - COMPLETE function usage and parameters
Architecture and Best Practices	10-15%	- Security and privacy considerations - Performance optimization techniques - Monitoring and evaluation frameworks - LLM pipeline architecture design - Cost management strategies
Data Preparation for Gen AI	15-20%	- Data governance for AI workloads - Vector stores and embeddings in Snowflake - Document processing and chunking strategies - Unstructured data handling
Cortex Analyst and Semantic Layer	20-25%	- Business logic implementation in semantic models - Text-to-SQL translation and optimization - Semantic model design and configuration - Performance tuning for analytical queries

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

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

2. 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) 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.
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) When a user shifts intent frequently in a multi-turn conversation, Cortex Analyst automatically resets the conversation history to prevent misinterpretations and improve accuracy.

3. An enterprise is deploying a new RAG application using Snowflake Cortex Search on a large dataset of customer support tickets. The operations team is concerned about managing compute costs and ensuring efficient index refreshes for the Cortex Search Service, which needs to be updated hourly. Which of the following considerations and configurations are relevant for optimizing cost and performance of the Cortex Search Service in this scenario?

A) The primary cost driver for Cortex Search is the number of search queries executed against the service, with the volume of indexed data (GB/month) having a minimal impact on overall billing.
B) The
C) For embedding text, selecting a model like

D) For optimal performance and cost efficiency, Snowflake recommends using a dedicated warehouse of size no larger than MEDIUM for each Cortex Search Service.
E) CHANGE_TRACKING

4. 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 an integer between 0 and 1 00, where higher values denote more positive sentiment.
B) The output will be a boolean value (TRUE/FALSE) indicating if the sentiment is positive, and 0.5 represents a neutral 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 a JSON object containing a 'label' field, and values around 0 indicate a neutral sentiment.
E) 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.

5. A data application developer is building a Streamlit chat application within Snowflake. This application uses a RAG pattern to answer user questions about a knowledge base, leveraging a Cortex Search Service for retrieval and an LLM for generating responses. The developer wants to ensure responses are relevant, concise, and structured. Which of the following practices are crucial when integrating Cortex Search with Snowflake Cortex LLM functions like AI_COMPLETE for this RAG chatbot?

A) To maintain conversational context in a multi-turn chat, the developer should pass all previous user prompts and model responses in the

B) For performance and cost optimization, it is always recommended to query Cortex Search and the LLM function within a single
C) Using the

D) The retrieved context from Cortex Search should be directly concatenated with the user's prompt as input to the
E) The