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Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A data engineer is integrating a custom application with Snowflake Cortex to leverage the 'COMPLETE' function via its REST API. They are preparing a 'curl' request to send a prompt to the 'mistral-large? model. Which of the following 'curl' command configurations correctly specifies the ''mandatory'' authentication header and a valid token type for accessing the Cortex REST API?

A)

B)

C)

D)

E)

2. An administrator has configured the 'CORTEX_MODELS_ALLOWLIST parameter to only permit the 'mistral-large? model at the account level. A user with the 'PUBLIC' role, which has been granted 'SNOWFLAKE.CORTEX USER and 'SNOWFLAKE."CORTEX- MODEL-ROLE-LLAMA3.1-70B"' , attempts to execute several 'AI COMPLETE queries. Which of the following queries will successfully execute?

A)

B)

C)

D)

E)

3. An enterprise is deploying a new RAG application using Snowflake Cortex Search on a large dataset of customer support tickets. The operations team is concerned about managing compute costs and ensuring efficient index refreshes for the Cortex Search Service, which needs to be updated hourly. Which of the following considerations and configurations are relevant for optimizing cost and performance of the Cortex Search Service in this scenario?

A) The primary cost driver for Cortex Search is the number of search queries executed against the service, with the volume of indexed data (GBImonth) having a minimal impact on overall billing.
B) For embedding text, selecting a model like

C) For optimal performance and cost efficiency, Snowflake recommends using a dedicated warehouse of size no larger than MEDIUM for each Cortex Search Service.
D) The
E) CHANGE_TRACKING

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

5. A data engineering team is building a pipeline in Snowflake that uses a SQL task to call various Snowflake Cortex LLM functions (e.g., AI_COMPLETE, AI EMBED) on large datasets of customer interaction logs. The team observes fluctuating costs and occasional query failures, which sometimes halt the pipeline. To address these issues and ensure an efficient, robust, and monitorable pipeline, which of the following actions or considerations are essential? (Select all that apply.)

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

Solutions: