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Snowflake GES-C01 Exam Syllabus Topics:

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A financial institution needs to process thousands of incoming PDF loan application forms daily, extracting applicant names, loan amounts, and submission dates, and loading them into a Snowflake table. They aim for continuous processing with minimal manual intervention. Which of the following statements correctly describe how Document AI can be used in an automated SQL pipeline for this purpose?

A) To ensure continuous data ingestion and processing, a STREAM can be created on the stage to detect new PDF documents, triggering the TASK for extraction and subsequent loading into a Snowflake table.
B) The extracted information, including confidence scores and values, is returned as a JSON object, which can then be parsed into separate columns in a Snowflake table using SQL functions like LATERAL FLATTEN.
C) Document AI's PREDICT method natively supports all PDF files up to 500 MB and 500 pages, allowing for large-scale, single-query processing without requiring users to split documents into smaller chunks.
D) The SNOWFLAKE .DOCUMENT_INTELLIGENCE_CREATOR database role alone is sufficient for defining the model build and configuring the processing pipeline, without needing additional CREATE MODEL privileges on the schema.
E) The pipeline can leverage the <model build name> ! PREDICT method within a CREATE TASK statement to automatically process new PDFs as they arrive in an internal or external stage, once the Document AI model build is published.

2. 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 B
B) Option E
C) Option A
D) Option D
E) Option C

3. A data science team is using SNOWFLAKE. CORTEX. CLASSIFY_TEXT to categorize product reviews into detailed segments like 'Bug Report - Critical', 'Feature Request - UI/UX', 'General Praise', or 'Query - Billing Issue'. For highly nuanced reviews, they find the initial classifications lack precision, and they are also concerned about the associated compute costs for processing large volumes of dat a. Which strategies should they employ to optimize classification accuracy and manage costs effectively with this function?

A) To reduce input token costs for classifications, the input text should be pre-processed to remove common stop words and punctuation, as these characters are counted as billable tokens without contributing to classification accuracy.
B) To improve accuracy for ambiguous classifications, they should augment the list_of_categories with explicit description and examples for each category, understanding that these additions will increase input token costs for each record processed.

C) If classifying thousands of reviews, they can significantly reduce overall compute costs by setting the temperature option to 0.0 within CLASSIFY_TEXT to ensure deterministic and cheaper inference.
D) For complex scenarios where the relationship between review text and categories is not straightforward, including a concise task_description (e.g., 'Classify the product review focusing on technical support relevance') in the options argument is recommended to guide the model.

E) CLASSIFY_TEXT labels, descriptions, and examples are counted as input tokens only once per function call, regardless of the number of records processed in a batch, to optimize cost efficiency.

4. A data engineer is building an AI data pipeline to automatically extract specific sentiment categories from customer reviews using 'AI_COMPLETE. They want the output to be a structured JSON object containing 'food_quality', 'food_taste', 'wait_time', and 'food cost' with their respective sentiments (e.g., 'positive', 'negative', 'neutral'). The engineer aims for high accuracy and ensures that all these fields are present in the output. Which of the following statements correctly describe the approach to achieve this?

A) The 'response_format' argument in 'AI COMPLETE should specify a JSON schema, and to ensure all fields are present, the 'required' field must be explicitly set for all desired properties in the schema.
B) The 'temperature' option should be set to a high value (e.g., 0.9) in the 'CompleteOptionS to encourage the model to strictly adhere to the specified JSON schema and reduce non-deterministic responses.
C) When using OpenAl (GPT) models for structured output, the 'additionalPropertieS field must be set to 'true' in every node of the schema to allow for flexibility in the generated JSON.
D) To improve accuracy, the JSON schema should include detailed descriptions for each field (e.g., 'food_quality', 'food_taste') to guide the model more precisely in identifying and extracting the sentiments.
E) For optimal accuracy in structured output, it is always best to explicitly instruct the LLM within the prompt to 'Respond in JSON' and provide a detailed description of the schema, regardless of task complexity.

5. A business intelligence team wants to enable non-technical users to query structured data in Snowflake using natural language. They are considering Cortex Analyst. What is the primary role of a semantic model in Cortex Analyst to achieve this goal for structured/text-to-SQL use cases?

A) The semantic model acts as a vector store, storing embeddings of all data columns to enable semantic search for natural language queries.
B) It stores user authentication credentials and data access policies, ensuring that only authorized users can interact with the data.
C) The semantic model directly executes SQL queries provided by end-users, bypassing the need for an LLM to generate them.
D) The semantic model provides a mapping between business-friendly terms and the underlying technical database schema, enhancing the LLM's ability to generate accurate SQL from natural language questions.
E) It serves as a cache for frequently requested data, reducing latency for natural language queries by providing pre-computed results.

Solutions: