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Snowflake GES-C01 Exam Syllabus Topics:

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A data engineering team is planning to build a real-time data pipeline using Snowflake's dynamic tables to process incoming log dat a. They want to use SNOWFLAKE. CORTEX. EXTRACT_ANSWER to pull out specific error codes and timestamps from log entries. They are also mindful of the operational costs. Which of the following statements accurately describes limitations or cost considerations for using SNOWFLAKE . CORTEX. EXTRACT_ANSWER in this scenario?

A) If a source_document input exceeds the model's token limit, EXTRACT_ANSWER will automatically truncate the text, potentially impacting results but avoiding an error.
B) EXTRACT_ANSWER is explicitly designed for multi-language extraction, making it suitable for logs that might contain various languages without affecting cost.
C) To optimize performance and reduce cost for large-scale log processing, it is recommended to execute EXTRACT_ANSWER queries on a Snowpark-optimized warehouse of at least 'LARGE' size.
D) The EXTRACT_ANSWER function does not support dynamic tables, which will prevent its direct use in this pipeline design.
E) The billing for EXTRACT_ANSWER is based on the combined token count of both the source_document (log entry) and the question used for extraction.

2. A data governance team is concerned about the consistency and compliance of SQL queries generated by Cortex Analyst for sensitive financial reporting. They need to ensure that all generated SQL for a specific semantic model always includes a 'WHERE' clause that filters data for 'region = 'EMEA" and adheres to 'ISO 8601' date formatting for all date columns, regardless of the user's natural language input. Which of the following approaches is the MOST effective for implementing these strict, overarching requirements within Cortex Analyst's semantic model?

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

3. An organization is planning to implement a new Retrieval Augmented Generation (RAG) application and has chosen Snowflake Cortex Search as its core retrieval engine. To effectively manage their budget, the finance and data teams need a clear understanding of the various cost components associated with deploying and operating a Cortex Search Service. Which of the following represent distinct cost categories directly attributable to the deployment and ongoing operation of a Snowflake Cortex Search Service?

A) Compute costs for LLM inference (e.g., SNOWFLAKE.CORTEX.COMPLETE) when the RAG application uses the retrieved context to generate responses.
B) Virtual warehouse compute used for refreshing the search service's index and processing base object changes.
C) Cloud Services compute for monitoring underlying base objects for changes to trigger search service refreshes.
D) ' Services compute specifically for generating vector embeddings of text data during the indexing and update processes.
E) Storage for the materialized source data and the optimized search index data structures within the Snowflake account.

4. A data scientist needs to fine-tune a 'mistral-7b' LLM using Snowflake Cortex for a specific text summarization task. They have prepared a training dataset in a Snowflake table, with text in a column named 'source_text' and the desired summaries in a column named 'expected_summary' . They also want to understand the cost implications. Which SQL statement will correctly initiate the fine-tuning job, and how will the cost be primarily calculated?

A) The fine-tuning job is initiated by:

B) The fine-tuning job is initiated by providing the prompt and completion data directly as arrays within the 'FINETUNE' function call, avoiding the need for a separate training data table, and costs are only for the storage of the fine-tuned model.
C) The fine-tuning job is initiated by:

D) The fine-tuning job is initiated by:

E) The fine-tuning job is initiated by:

5. A company wants to ingest and process scanned invoices and digitally-born contracts in Snowflake. They need to extract all text, preserving layout for contracts and just the text content for scanned invoices. Which AI_PARSE_DOCUMENT modes would be most appropriate for this scenario, and what is the primary purpose of the function itself?

A) Primary purpose is to classify text. For contracts, use LAYOUT mode; for invoices, use OCR mode.
B) Primary purpose is to summarize text. For contracts, use OCR mode; for invoices, use LAYOUT mode.
C) Primary purpose is to generate new text. For contracts, use OCR mode; for invoices, use LAYOUT mode.
D) Primary purpose is to extract data and layout. For contracts, use LAYOUT mode; for invoices, use OCR mode.
E) Primary purpose is to translate text. Both document types should use LAYOUT mode.

Solutions: