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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. A data science team is deploying a custom real-time inference service for a fine-tuned LLM using Snowpark Container Services (SPCS). They have a Docker image in their Snowflake image repository. They need to define the service using a YAML specification file. Which of the following are ''essential'' components or configurations that must be included in the 'spec.yaml' file for a long- running service that uses this image, custom environment variables, and requires external access?

A)

B)

C)

D)

E)

3. A Gen AI Specialist is tasked with implementing a data pipeline to automatically enrich new customer feedback entries with sentiment scores using Snowflake Cortex functions. The new feedback arrives in a staging table, and the enrichment process must be automated and cost-effective. Given the following pipeline components, which combination of steps is most appropriate for setting up this continuous data augmentation process?

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

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

5. A financial institution is fine-tuning a llama3.1-70b model within Snowflake Cortex using sensitive internal financial reports to improve sentiment analysis on earnings call transcripts. They need to understand the implications for data privacy, model ownership, and how this fine-tuned model can be managed and shared. Which of the following statements are true regarding this process?

A) The fine-tuning process requires the explicit provisioning and management of a Snowpark-optimized warehouse with GPU resources by the institution.
B) The resulting fine-tuned model (e.g., my_sentiment_model) is the exclusive property of the financial institution and cannot be accessed or used by any other Snowflake customer.
C) The fine-tuned model, being of type CORTEX_FINETUNED, can be shared with other Snowflake accounts using secure data sharing capabilities.
D) The financial reports used for fine-tuning the llama3.1-7b model are securely isolated and are not used by Snowflake to train or re-train models for other customers.
E) Fine-tuned LLMs built with Cortex Fine-tuning are fully managed through the Snowflake Model Registry API, allowing for programmatic deployment, version control, and comprehensive lifecycle management.

Solutions: