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Snowflake GES-C01 Exam Syllabus Topics:

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

Question 1

A financial analytics team is developing an application to extract specific, structured financial data (e.g., company name, revenue, profit margin) from various news articles using Snowflake Cortex LLM functions. They require the output to strictly conform to a predefined JSON schema and want to ensure robust error handling. Which of the following statements are crucial considerations for achieving this goal?

A. To guarantee that critical fields like 'company name' and 'revenue' are always extracted, these properties must be explicitly listed within the ' required' array of the JSON schema provided to AI_COMPLETE.
B. The complexity of the JSON schema provided to AI_COMPLETE has no impact on compute costs, as only the input text and generated content tokens are billed.
C. For enhanced reliability in production pipelines, the team should wrap their AI_COMPLETE calls within TRY_COMPLETE, as it returns a structured error object if the model fails to adhere to the schema, allowing for detailed debugging.
D. The AI_COMPLETE function should be used with the response_format argument, supplying a JSON schema object that defines the required structure, data types, and constraints for the output.
E. Setting the temperature option to 0 in the AI_COMPLETE call is essential for obtaining the most consistent and accurate structured JSON outputs, regardless of task complexity or model used.

Question 2

A data engineering team is setting up a new Cortex Search Service named to power a RAG application over their table, which stores historical ticket text and metadata. They need to ensure proper setup, cost efficiency, and data integrity. Which of the following statements are true regarding the creation and initial configuration of this Cortex Search Service? (Select all that apply)

A. If the service is created using the Snowsight AI & ML Studio, its name will be double-quoted, and thus must be double-quoted when referenced in subsequent SQL queries.
B. To enable continuous updates of the search index as new tickets are added,

C. The role used to create the Cortex Search Service must be granted the 'SNOWFLAKE.CORTEX_USER database role.
D. The 'CREATE CORTEX SEARCH SERVICE command should specify a Snowpark-optimized warehouse for optimal performance, as it is designed for memory-intensive ML workloads.
E. Columns intended to be filterable in search queries must be explicitly listed in the 'ATTRIBUTES' field during service creation and must also be included in the source query for the service.

Question 3

A security auditor needs to access and analyze logs generated by Snowflake AI Observability for compliance auditing and to track the activity of generative AI applications. They need to understand how to reliably query this data and its temporal characteristics within Snowflake. Which of the following statements accurately describes the access and characteristics of this logged data?

A. Logs are exclusively available for analysis through pre-built dashboards in Snowsight and cannot be accessed via direct SQL queries.
B. The logs are automatically purged after 7 days of being recorded, requiring a separate process for long-term data retention.
C. Access to these detailed event tables is implicitly granted to roles holding the SNOWFLAKE. CORTEX_USER database role and the AI_OBSERVABILITY_EVENTS_LOOKUP application role.
D. Detailed request and response bodies, along with the generated SQL, are stored and can be directly queried using standard SQL.
E. Logged data from AI Observability's event tables becomes visible within a small latency, typically 1-2 minutes, after a request is made.

Question 4

A data engineer is developing an AI-infused data pipeline in Snowflake Notebooks to analyze Federal Reserve Meeting Minutes and official Statements, which are initially in PDF format. The goal is to determine the FED's stance on interest rates (hawkish, dovish, or neutral) and the reasoning for each ingested PDF using an LLM. The pipeline needs to automate data ingestion, text extraction, LLM inference, and store the results in a Snowflake table. Which sequence of operations and Snowflake features is most appropriate for building this pipeline within Snowflake?

A. Load unstructured PDF files into an internal stage. Use a stored procedure to download new PDFs from the FOMC website. Leverage Snowpark Container Services to deploy a fine-tuned open-source LLM (e.g., Llama 2) for text extraction and sentiment analysis, and orchestrate the pipeline with ' Dynamic TableS for continuous updates.
B. Directly ingest PDF documents into a 'VARIANT column in a Snowflake table. Then, use the SQL function in 'OCR mode to extract text and layout. The extracted text is then passed to 'SNOWFLAKE.CORTEX.CLASSIFY TEXT to determine the sentiment, and the results are stored in a new table.
C. Scrape PDF data from an external website, load unstructured PDF files to an internal stage, then use a 'UDE to parse raw text from PDFs and a separate UDF' ('GENERATE_PROMPT) to encapsulate a custom prompt. Finally, use a 'TASK' to automate the process, calling Snowflake's function with the custom prompt at the point of ingestion to generate the sentiment signal and reasoning.
D. Scrape data from an external website directly into a Snowflake table using an 'EXTERNAL FUNCTION'. Then, apply 'SNOWFLAKE.CORTEX.EXTRACT ANSWER with a question like 'What is the FED's stance?' and 'SNOWFLAKE.CORTEX.SUMMARIZE' for reasoning to enrich the table. Automate this using 'STREAMS' and 'TASKS.
E. Ingest PDF documents into a directory table. Use 'Document AI' C!PREDICT') to extract specific entities and tables from the PDFs into structured JSON. Then, create a 'STREAM' on the stage and a 'TASK' to continuously process new documents, extracting information and potentially performing additional sentiment analysis with another LLM.

Question 5

A data architect is designing a workflow to programmatically extract highly structured data from various text inputs using Snowflake Cortex AI's AI_COPIPLETE function through its REST API. They require the output to strictly adhere to a complex JSON schema for downstream processing and need to manage associated costs. Which of the following statements accurately describe aspects of this approach?

A. Option C
B. Option A
C. Option D
D. Option E
E. Option B

Solutions: