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Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A data engineering team is building a pipeline to process legal documents using Snowflake Cortex functions. They aim to extract specific entities and summarize key clauses while being highly cost-conscious. To optimize token-based costs, which of the following practices should they implement when using Cortex LLM functions?

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

2. Considering Snowflake's Gen AI principles for cost governance within Snowflake Cortex, an ML engineer is assessing the expenditure for an LLM fine-tuning job. Which option correctly identifies how compute costs for Cortex Fine-tuning are primarily incurred and how fine-tuned models are treated regarding usage by other customers?

A) Compute costs for fine-tuning are based on the number of tokens used in training, calculated as 'number of input tokens number of epochs trained'. Fine-tuned models built using a customer's data are available exclusively for that customer's use.
B) Costs are incurred per hour of compute pool usage, similar to virtual warehouses. Fine-tuned models are anonymized and used to train future foundation models for all customers.
C) Fine-tuning costs are a flat monthly fee, irrespective of token usage or model size. Fine-tuned models become part of Snowflake's proprietary models after training.
D) Only inference using fine-tuned models incurs costs, not the training itself. Fine-tuned models can be openly shared on the Snowflake Marketplace.
E) Costs are based on the number of fine-tuning jobs created, not tokens. Fine-tuned models are shared across all Snowflake customers to improve the general service.

3. 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) 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.
B) 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.
C) 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.
D) 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.
E) 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.

4.
After resolving this, they try to process a batch of 1500 documents in a single query using the method, which also fails. Which two issues are most likely contributing to these failures?

A) The virtual warehouse being used for Document AI is not a Snowpark-optimized warehouse.
B) The has not been granted the 'CREATE STREAM' privilege on the
C) The batch of documents exceeds Document AI's limit of 1000 documents per query.
D) The name is not unique within the .
E) The 'SNOWFLAKE.DOCUMENT INTELLIGENCE CREATOR database role or the 'CREATE SNOWFLAKE.ML.DOCUMENT INTELLIGENCE' privilege on the schema has not been granted to the .

5.

A)

B)

C)

D)

E)

Solutions: