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Snowflake GES-C01 Exam Syllabus Topics:

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A data engineering team is optimizing an AI-infused pipeline that processes millions of rows of customer interaction data in a LOG_DATA table using various Snowflake Cortex AI functions. They need to accurately estimate costs and ensure optimal performance. Which of the following statements regarding cost, performance, and operational considerations for these functions are true?

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

2. A Gen AI Specialist is leveraging Snowflake Document AI to extract specific entities and table data from a large and varied collection of documents. They are aware of potential limitations and want to understand the expected outcomes when processing different types of files. Considering a scenario where a Document AI model build is used with the '!PREDICT' method, which of the following statements accurately describe the expected behavior or potential issues based on Document AI's conditions and limitations?

A) If the extracted answer to a question for a single entity (e.g., is very long, it will be automatically truncated to a maximum of 2048 tokens.
B) If a question for an entity, like 'total_invoice_amount', does not find a corresponding value in a document, the JSON output for will contain a 'value' key with a 'null' string and a 'score' key indicating the model's confidence in the absence of the answer.
C) Processing a legal contract document that is 130 pages long will likely result in a '_processingErrors' message indicating that the document has too many pages.
D) A document written entirely in Ukrainian will be processed by Document AI, and the extracted information will be of satisfactory quality due to extensive multilingual support.
E) In a table extraction task, if a specific cell (e.g., 'tablellitem') is empty, the resulting JSON will omit the 'value' key for that cell, but will still provide a 'score' indicating the model's confidence that the cell is empty.

3. 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) 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.
B) For monitoring agent interactions and performance on the client application, the TruLens Python packages (

C) 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.
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) 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.

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

5. A data engineer is integrating SNOWFLAKE. CORTEX. CLASSIFY_TEXT into an automated data pipeline that uses dynamic tables to process and transform streaming text dat a. They have ensured that the service account used has been granted the necessary SNOWFLAKE. CORTEX_USER database role. After deploying the pipeline, they consistently receive an error whenever CLASSIFY_TEXT is invoked. Which of the following is the most likely cause of the error encountered by the data engineer?

A) The input text being processed by 'CLASSIFY _ TEXT includes extensive non-plain English content, such as code blocks, which causes the function to fail with an error.
B) The role used by the data engineer, despite having 'SNOWFLAKE.CORTEX_USER, lacks the fundamental 'USAGE privilege on the database where the text data is stored.
C) Snowflake Cortex functions, including 'CLASSIFY_TEXT , currently do not support integration with dynamic tables within data pipelines.
D) The array contains more than 100 unique categories, exceeding the maximum allowed limit for the function.
E) The 'task_description' provided in the optional arguments for 'CLASSIFY_TEXT exceeds the recommended length of approximately 50 words, leading to a validation error.

Solutions: