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Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1.
database role. Which of the following 'privileges directly related to the virtual warehouse or task execution' are required for the process invoices_task' to successfully execute the Document AI '!PREDICT' method? (Select ALL that apply.)

A)

B)

C)

D)

E)

2.

A) The USAGE privilege on the specific database and schema where the Streamlit application and its underlying data tables are located.
B)

C)

D) The CREATE COMPUTE POOL privilege to provision resources for the Streamlit application.
E) The ACCOUNTADMIN role to ensure unrestricted access to all Snowflake Cortex features.

3. A project team is preparing to deploy a Document AI solution to process scanned customer feedback forms. They have created a dedicated role, 'customer feedback _ processor', and successfully granted it the SNOWFLAKE. DOCUMENT_INTELLIGENCE_CREATOR database role. The environment consists of 'feedback database, 'forms schema' schema, and 'ai workload warehouse. However, when the attempts to prepare a Document AI model build in Snowsight, they encounter a 'permission denied' error. Which of the following missing 'USAGE' grants could be the direct cause of this error?

A)

B)

C)

D)

E)

4. A data science team is developing an internal LLM to classify legal documents. They previously used a general-purpose LLM, but found its performance for their specific legal domain to be inconsistent, leading to high error rates and increased manual review. They decide to fine-tune a model using Snowflake Cortex Fine-tuning to improve accuracy and reduce latency for real-time document classification. Which base model, among those available for fine-tuning via SNOWFLAKE .CORTEX.FINETUNE
, is explicitly noted for its low latency and high throughput processing, making it a strong candidate for this use case, especially for multi-page text classification?

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

5. A company is building an enterprise search solution in Snowflake, where user queries are converted into embeddings and then used to find relevant documents from a large corpus. The search logic heavily relies on VECTOR_COSINE_SIMILARITY Which of the following design choices or operational considerations are critical for a robust and efficient implementation using Snowflake's vector capabilities? (Select all that apply)

A) For improved retrieval quality in RAG scenarios, it is recommended to split text into smaller chunks, ideally no more than 512 tokens, before generating embeddings for subsequent
B) Storing document embeddings in a
C) When deploying custom embedding models or complex search logic, Snowpark Container Services can host GPU-accelerated environments, while
D) To keep document embeddings updated efficiently, a
E) Bind variables can be used to pass query vector literals securely and efficiently to

Solutions: