Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A retail company wants to implement an automated data pipeline in Snowflake to analyze daily customer reviews. The goal is to enrich a 'product_reviews_sentiment' table with sentiment categories (e.g., 'positive', 'neutral', 'negative') for each new review. They require the sentiment to be returned as a JSON object for downstream processing and need the pipeline to handle potential LLM errors gracefully without stopping. Assuming a stream 'new reviews_stream' monitors a 'customer _ reviews' table, which approach effectively uses a Snowflake Cortex function for this scenario?

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

2. A Streamlit application developer wants to use AI_COMPLETE (the latest version of COMPLETE (SNOWFLAKE.CORTEX)) to process customer feedback. The goal is to extract structured information, such as the customer's sentiment, product mentioned, and any specific issues, into a predictable JSON format for immediate database ingestion. Which configuration of the AI_COMPLETE function call is essential for achieving this structured output requirement?

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

3. An operations team at a company is implementing a robust governance framework to monitor and optimize the costs associated with their Snowflake Cortex LLM function usage. They need to identify which functions are driving the highest token consumption and overall credit usage to pinpoint areas for cost reduction. Which of the following monitoring tools or methods are appropriate for gaining these insights into Cortex LLM function costs and token consumption?

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

4.
database role. Which of the following 'privileges directly related to the virtual warehouse or task execution' are required for the process invoices_task' to successfully execute the Document AI '!PREDICT' method? (Select ALL that apply.)

A)

B)

C)

D)

E)

5. 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) 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.
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) 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.
E) 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.

Solutions: