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Snowflake GES-C01 Exam Syllabus Topics:

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A data analytics team aims to enhance their understanding of customer feedback stored in a Snowflake table called CUSTOMER_FEEDBACK. This table has a REVIEW_TEXT column containing raw customer comments and a CUSTOMER_SEGMENT column. The team wants to classify each review into predefined categories and then generate a concise summary of all reviews for each customer segment. Which of the following Snowflake Cortex AI functions and approaches should they use?

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

2. A data governance team is concerned about the consistency and compliance of SQL queries generated by Cortex Analyst for sensitive financial reporting. They need to ensure that all generated SQL for a specific semantic model always includes a 'WHERE' clause that filters data for 'region = 'EMEA" and adheres to 'ISO 8601' date formatting for all date columns, regardless of the user's natural language input. Which of the following approaches is the MOST effective for implementing these strict, overarching requirements within Cortex Analyst's semantic model?

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

3. An ML engineer has developed a custom PyCaret classification model and wants to deploy it to Snowpark Container Services (SPCS) for inference using the Snowflake Model Registry. The model requires specific versions of pycaret' , 'scipy', and 'joblib'. The engineer also wants to make the service accessible via an HTTP endpoint. Which of the following Model Registry and service creation steps are 'most appropriate' for the ML engineer? (Select all that apply.)

A)

B)

C)

D)

E) Opt for warehouse deployment instead of SPCS, as PyCaret is not natively supported by Snowflake and managing its dependencies in SPCS would be overly complex compared to a warehouse.

4. 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) 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.
B) 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.
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) 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.
E) Only inference using fine-tuned models incurs costs, not the training itself. Fine-tuned models can be openly shared on the Snowflake Marketplace.

5. A data scientist wants to fine-tune a
mistral -7b
model to improve its ability to generate specific product descriptions based on brief input features. They have a table named PRODUCT_CATALOG with columns PRODUCT_FEATURES (text) and GENERATED_DESCRIPTION (text). Which of the following statements correctly describe the preparation and initiation of this fine-tuning job in Snowflake Cortex?
(Select all that apply)

A) The
B) O To generate highly structured
C) The fine-tuning job must be created using a
D) The SQL query for the training data must select columns aliased as
E) Once a fine-tuned model is created, it is fully managed by the Snowflake Model Registry API, allowing for programmatic updates to its parameters and versions.

Solutions: