Snowflake SnowPro® Specialty: Gen AI Certification : GES-C01

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Snowflake GES-C01 Exam Syllabus Topics:

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

Question 1

A data engineering team is tasked with improving the accuracy of a Cortex Analyst solution for a large e-commerce product catalog. Users frequently ask natural language questions involving specific product names, brands, and categories. The team observes that Cortex Analyst sometimes struggles to identify and correctly filter by these literal values in the generated SQL. Which of the following configurations or approaches, within the semantic model, can effectively enhance Cortex Analyst's ability to precisely identify and use literal values for filtering, based on Snowflake's best practices?

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

Question 2

A.

B.

C.

D. Cross-region inference is automatically managed by Snowflake for allowed models, implying that a new, larger virtual warehouse is required to handle the cross- region data transfer overhead.
E.

Question 3

A financial institution uses Snowflake Cortex Analyst with strict role-based access control (RBAC) on their Snowflake-hosted LLMs. The security team has granted specific 'CORTEX-MODEL-ROLE application roles to different analyst teams, ensuring they only access approved models. A new requirement arises to enable Azure OpenAI GPT models for Cortex Analyst to leverage a specific feature. An administrator proceeds to execute:

Which of the following statements accurately describe the implications of this change?

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

Question 4

A financial services company uses Snowflake Cortex's AI_COMPLETE for sentiment analysis on customer call transcripts, which contain personally identifiable information (PII). They also fine-tune a llama3.1-70b model with proprietary financial data. Which of the following statements accurately describe Snowflake's Gen AI principles regarding data privacy, model usage, and governance in this scenario?

A. The fine-tuned llama3.1-70b model, including the proprietary training data used, is exclusively owned by the financial institution and is not shared with other Snowflake customers.
B. Enabling Cortex Guard for AI_COMPLETE automatically anonymizes all PII within the prompt before it reaches the LLM, regardless of the model chosen, to ensure privacy.
C. Customer Data (inputs and outputs) for AI_COMPLETE, including PII, are guaranteed not to be available to other customers or used to train models made available to others.
D. When using Cortex Analyst with Snowflake-hosted LLMs, metadata and prompts are transmitted outside Snowflake's governance boundary for processing, incurring additional cross-cloud data transfer costs.
E. Snowflake's metadata fields, such as table and column names, should not contain personal, sensitive, or export-controlled data when using Snowflake AI services, to maintain data governance.

Question 5

A data engineer is designing an automated pipeline to process customer feedback comments from a 'new_customer_reviews' table, which includes a 'review_text' column. The pipeline needs to classify each comment into one of three predefined categories: 'positive', 'negative', or 'neutral', and store the classification label in a new 'sentiment_label' column.
Which of the following statements correctly describe aspects of implementing this data transformation using 'SNOWFLAKE.CORTEX.CLASSIFY_TEXT' in a Snowflake pipeline?

A. The argument must contain exactly three unique categories for sentiment classification.
B. Including an optional 'task_description' such as

C. The classification can be achieved by integrating a 'SELECT statement with

D. The cost for 'CLASSIFY _ TEXT is incurred based on the number of pages processed in the input document.
E. Both the input string to classify and the are case-sensitive, potentially yielding different results for variations in capitalization.

Solutions: