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Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A data engineer is setting up a Document AI pipeline to extract information from scanned invoices stored in an internal stage named 'invoice_stage'. They have created the stage using 'CREATE STAGE and uploaded several PDF documents. However, when attempting to run the extraction query, they encounter an error message: 'File extension does not match actual mime type. Mime- Type: application/octet-stream'. Additionally, they anticipate a privilege issue might arise for pipeline automation. Which of the following conditions must be met to resolve the current error and ensure proper setup for Document AI extraction and subsequent pipeline creation?

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

2. A Data Scientist has a pre-trained PyCaret model and wants to log it into the Snowflake Model Registry for inference. The model requires specific versions of and 'scipy' , and a configuration file 'my_config.json' needs to be packaged with the model for use during inference. Assuming 'sp_session' is an active Snowpark Session, is an instance of 'PyCaretModel' , and 'train_features' is a Pandas DataFrame for which of the following code snippets correctly logs this custom PyCaret model into the Snowflake Model Registry?

A)

B)

C)

D)

E)

3. A data engineer is developing a Snowflake Cortex LLM application that processes sensitive customer feedback. To ensure that generated responses from the 'COMPLETE' function are filtered for potentially unsafe or harmful content, they need to enable Cortex Guard. Which of the following SQL 'COMPLETE function calls correctly demonstrates the enablement of Cortex Guard with the default unsafe response message?

A)

B)

C)

D)

E)

4. A data analyst is using Snowflake Copilot in Snowsight to generate SQL queries for a new dataset containing customer PII. Which of the following statements accurately describes how Snowflake Copilot operates with respect to data access, governance, and model interaction?

A) Snowflake Copilot directly accesses and processes the raw data within customer tables to understand its content and generate SQL.
B) To protect sensitive information, Snowflake Copilot transmits sampled PII data to an external LLM for schema understanding before generating SQL.
C) While Snowflake Copilot generates SQL based on metadata, the generated SQL queries are executed in an isolated environment that does not respect existing Snowflake RBAC policies.
D) Snowflake Copilot requires explicit column-level grants for direct data access, similar to how a human analyst would query specific data points.
E) Snowflake Copilot is powered by a fine-tuned model that runs securely inside Snowflake Cortex, leveraging only database/schema/table/column names and data types, ensuring data remains within Snowflake's governance boundary and respects RBAC.

5. A Gen AI developer is using 'SNOWFLAKE.CORTEX.COMPLETE" to generate concise summaries of legal documents. Initially, the LLM sometimes provides overly creative or slightly off-topic responses, indicating potential 'hallucinations' or a lack of focus. To improve the factual accuracy and conciseness of the summaries, which combination of prompt engineering techniques and 'COMPLETE' function options should be prioritized?

A) Implement 'first principles thinking' in the prompt, clearly outlining logical steps for summarization, and set 'temperature' to 0 for deterministic output.
B) Rely solely on a comprehensive list of 'stop sequences' to end generation when the summary is complete.
C) Use a system prompt defining a persona like 'creative writer' and set 'temperature' to 0.9 to encourage diverse summaries.
D) Instruct the model to 'think out loud' with an inner monologue in the prompt, and set 'max_tokens' to a large value to allow full reasoning.
E) Provide a 'task description' focusing on broad themes and set 'top_p' to 0.5 to balance creativity and relevance.

Solutions: