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Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:
1. A financial institution wants to automate the extraction of key entities (e.g., invoice number, total amount, list of invoice items) from incoming PDF financial statements into a structured JSON format within their Snowflake data pipeline. The extracted data must conform to a specified JSON schema for seamless downstream integration. Which Snowflake Cortex capabilities, when combined, can best achieve this data augmentation and ensure schema adherence in a continuous processing pipeline?
A) Option D
B) Option A
C) Option C
D) Option B
E) Option E
2. A Snowflake Gen AI Specialist is defining a semantic model for Cortex Analyst to improve text-to-SQL accuracy. They are adding entries to the verified _ queries section of their YAML file. Consider the following semantic model snippet and a proposed verified_query entry. Which of the following statements correctly identifies an issue or a best practice not followed in the sql field of the proposed verified_query entry, based on Cortex Analyst VQR guidelines? Semantic Model Snippet:
Proposed verified _ query entry:

A) Option D
B) Option A
C) Option C
D) Option B
E) Option E
3. A data application developer, adhering to Snowflake's Gen AI best practices for deploying LLMs, needs to perform inference with a newly fine-tuned llama3.1-70b model via AI_COMPLETE and expects a structured JSON output. Which of the following statements accurately describe how to configure this inference and potential limitations within Snowflake Cortex?
A) Option D
B) Option A
C) Option C
D) Option B
E) Option E
4. A data engineering team is designing a Snowflake data pipeline to automatically enrich a 'customer issues' table with product names extracted from raw text-based 'issue_description' columns. They want to use a Snowflake Cortex function for this extraction and integrate it into a stream and task-based pipeline. Given the 'customer_issues' table with an 'issue_id' and (VARCHAR), which of the following SQL snippets correctly demonstrates the use of a Snowflake Cortex function for this data enrichment within a task, assuming is a stream on the 'customer issues' table?
A) Option D
B) Option A
C) Option C
D) Option B
E) Option E
5. A financial analyst wants to build a generative AI application in Snowflake that can answer complex queries by integrating financial reports (unstructured data in stages) and transaction records (structured data in tables). They decide to use Snowflake Cortex Agents. Which of the following statements accurately describe the capabilities and operational aspects of Cortex Agents in this scenario?
(Select all that apply)
A) To provide the Agent with custom logic for specific data transformations not covered by standard tools, stored procedures or user-defined functions (UDFs) can be implemented as custom tools.
B) The primary compute cost for Cortex Agents is based on the number of tokens processed during the planning and reflection phases, with an additional per- message charge for each tool invocation.
C) When a user asks an ambiguous question, Cortex Agents utilise an 'Explore options' component to consider different permutations and disambiguate the query for improved accuracy.
D) Cortex Agents are designed to orchestrate tasks by planning steps, utilising tools like Cortex Analyst for structured data and Cortex Search for unstructured data, and generating comprehensive responses.
E) For monitoring agent interactions and performance on the client application, the TruLens Python packages (
Solutions:
| Question # 1 Answer: A,D | Question # 2 Answer: C,D | Question # 3 Answer: A,D | Question # 4 Answer: D | Question # 5 Answer: A,C,D,E |



