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Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A data engineer is configuring a Document AI pipeline to process scanned PDF invoices stored in an internal stage named 'invoice_docs_stage'. After uploading the PDF files, they execute an extracting query using '!PREDICT. The query consistently returns the error:

Which of the following is the most likely cause of this error?

A) The 'GET_PRESIGNED_URL' function used in the '!PREDICT query has an expired URL.
B) The documents contain non-English text, which is not fully supported by Document AI for optimal results.
C) The internal stage was not created with 'SNOWFLAKE_SSE' encryption enabled.
D) The Document AI model build is attempting to process more than 1000 documents in a single query.
E) The PDF documents exceed the maximum allowed file size of 50 MB.

2.

A) The ACCOUNTADMIN role to ensure unrestricted access to all Snowflake Cortex features.
B)

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

E) The CREATE COMPUTE POOL privilege to provision resources for the Streamlit application.

3. A Streamlit application developer wants to use AI_COMPLETE (the latest version of COMPLETE (SNOWFLAKE. CORTEX)) to process customer feedback. The goal is to extract structured information, such as the customer's sentiment, product mentioned, and any specific issues, into a predictable JSON format for immediate database ingestion. Which configuration of the AI_COMPLETE function call is essential for achieving this structured output requirement?

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

4. A data scientist has developed a Hugging Face sentence transformer model for semantic search and needs to deploy it for GPU- powered inference using Snowpark Container Services (SPCS) in Snowflake. They've already trained the SentenceTransformer model locally. Which of the following statements correctly describe essential considerations for logging and deploying this model, ensuring it leverages GPU resources and appropriate dependencies?

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

5. An enterprise is deploying a Cortex Analyst application and needs to manage its cost, ensure data security, and understand its operational behavior within Snowflake. Which of the following statements are true regarding the deployment, cost, and security of Cortex Analyst and its semantic models?

A) Administrators can monitor Cortex Analyst requests, including the user, question asked, generated SQL, and errors, by querying the SNOWFLAKLOCAL .CORTEX_ANALYST_REQUESTS function.
B) Snowflake strongly recommends enabling the ENABLE_CORTEX_ANALYST_MODEL_AZURE_OPENAI account parameter to leverage Azure OpenAI models for Cortex Analyst, as it offers the highest performance and respects RBAC restrictions for these models.
C) Semantic models for Cortex Analyst, whether stored as YAML files or native semantic views, should have their access controlled by RBAC. This implicitly controls access to the underlying tables referenced in the semantic model.
D) The primary cost incurred for Cortex Analyst is based on the number of tokens processed by the underlying LLMs, with more complex natural language questions directly leading to higher token usage and charges.
E) When using Snowflake-hosted LLMs (e.g., from Mistral or Meta) with Cortex Analyst, all customer data, including metadata and prompts, remains within Snowflake's governance boundary.

Solutions: