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Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A data analytics team is building a self-service analytics application using Snowflake Cortex Analyst to allow business users to query sales data with natural language. They are defining a semantic model in YAML to ensure accurate text-to-SQL generation. Which of the following is the most crucial aspect of the semantic model's configuration for Cortex Analyst to effectively translate natural language into SQL for structured data?

A) Utilizing advanced data types like 'VARIANT' and 'OBJECT for all dimensions to accommodate semi-structured data without complex transformations.
B) Specifying a dedicated 'CORTEX SEARCH SERVICE for every dimension to pre-compute all possible literal values, optimizing response time.
C) Providing detailed 'name', 'description' , and 'synonyms' for logical tables, dimensions, and facts to bridge the gap between business terminology and the underlying database schema.
D) Defining a comprehensive 'verified_queries' section with a high volume of example natural language questions and their exact SQL translations to handle all potential user queries.
E) Configuring the 'base_table' parameter to directly reference a dynamic table, ensuring real-time data ingestion and processing before SQL generation.

2. A multinational corporation is implementing Document AI to automate the processing of purchase orders from various global suppliers. These purchase orders vary significantly in layout and are often submitted in English, German, and Spanish. The data engineering team aims to optimize the preparation phase for effective model training and deployment. Considering Document AI's 'Question optimization best practices' and general document preparation guidelines, which of the following is a 'critical consideration' for successful implementation?

A) Document AI model builds can only support a single document layout type; therefore, separate model builds must be created for each distinct purchase order layout from different suppliers.
B) For multilingual support, it is mandatory to externally translate all non-English purchase orders to English before uploading them to an internal stage, as
C) To simplify prompt engineering, define generic questions such as 'What is the total amount?' irrespective of document layout, as Document AI's foundation model is expected to handle most layout variations through its zero-shot capabilities.
D) For maximum efficiency in defining data values for complex extractions (e.g., lists of line items), prioritize spending extensive time on crafting highly precise and detailed natural language prompts to guide the model.
E) The training dataset should be highly diverse, representing various layouts, data variations (including potential NULLs), and containing documents in all target languages (English, German, Spanish) to ensure robust model performance.

3. A Gen AI developer is implementing a Cortex Search Service for a RAG application and needs to configure the text splitting for optimal performance using SNOWFLAKE.CORTEX.SPLIT_TEXT_RECURSIVE_CHARACTER Which of the following statements represent best practices or outcomes when applying text splitting with this function for Cortex Search in a RAG scenario? (Select all that apply)

A) Smaller chunk sizes generally lead to higher retrieval precision for a given query in a RAG system.
B) The function automatically enriches each text chunk with relevant metadata about its original document, such as author and creation date, for enhanced filtering capabilities in Cortex Search.
C) Snowflake recommends splitting text into chunks of no more than 512 tokens for best search results in Cortex Search.
D) Even when using embedding models with larger context windows (e.g., 8000 tokens), a smaller chunk size is typically preferred for improved retrieval and downstream LLM response quality.
E) Optimal text splitting using this function ensures that the number of input tokens precisely equals the number of output tokens for subsequent LLM calls, thereby minimizing compute costs.

4. A data engineering team needs to implement a highly accurate, low-latency solution for classifying specialized technical documents into 50 distinct categories. They are considering fine-tuning a Large Language Model (LLM) within Snowflake Cortex for this task. Which of the following considerations are critical for optimizing the fine-tuned model's performance and minimizing inference latency for production use? (Select all that apply)

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

5. A team is developing a critical business intelligence application that leverages Snowflake Cortex Analyst to provide natural language querying capabilities over complex structured dat a. To minimize operational costs while maintaining high accuracy, which of the following strategies are most effective for optimizing the cost efficiency of the Cortex Analyst service?

A) Implementing a comprehensive Verified Query Repository (VQR) to guide Cortex Analyst towards pre-validated SQL queries for common questions, which ensures predictable execution and reduces LLM inference iterations.
B) Using a smaller, less capable LLM as the underlying summarization agent for multi-turn conversations to reduce token processing costs, even if it slightly degrades conversational context.
C) Configuring a custom instruction with a short, precise task description to reduce the input token count for the LLMs orchestrating SQL generation.
D) Optimizing the semantic model YAML file by reducing the number of logical tables and columns to decrease the metadata processed by Cortex Analyst's LLMs per message.
E) Leveraging Cortex Search Services integration within the semantic model to improve literal value matching, thereby reducing the need for Cortex Analyst to perform expensive fuzzy string matching or re-prompt the user.

Solutions: