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Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A marketing team is analyzing social media comments using Snowflake and wants to categorize them into predefined campaign sentiments (e.g., 'Positive Campaign Engagement', 'Negative Campaign Feedback', 'Neutral Discussion'). They decide to use the SNOWFLAKE. CORTEX. CLASSIFY TEXT function for this task. Which of the following statements about its usage are correct?

A) CLASSIFY_TEXT can return a JSON object with a 'label' field, where the value of this field indicates the classified category of the input text.
B) If the input text exceeds a model-specific token limit, CLASSIFY_TEXT will automatically truncate the text before processing without raising an error.
C) To provide more context and potentially improve classification accuracy, categories within the can be defined as SQL objects, including 'description' and 'examples' fields.
D) The input string to CLASSIFY_TEXT is case-insensitive, meaning 'Great product!' and 'great product!' will yield identical classification results due to automatic normalization.
E) The argument must contain exactly two string values for effective binary classification, otherwise an error is returned.

2. 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) 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.
B) The fine-tuned model, being of type CORTEX_FINETUNED, can be shared with other Snowflake accounts using secure data sharing capabilities.
C) The fine-tuning process requires the explicit provisioning and management of a Snowpark-optimized warehouse with GPU resources by the institution.
D) 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.
E) 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.

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

A)

B)

C)

D)

E)

4. A data architect is integrating Snowflake Cortex LLM functions into various data enrichment pipelines. To ensure optimal performance, cost-efficiency, and accuracy, which of the following are valid best practices or considerations for these pipelines?

A) For tasks requiring deterministic JSON outputs, explicitly specifying a JSON schema using the 'response_format' argument with 'AI COMPLETE is crucial, and for OpenAI (GPT) models, including the 'required' field and setting 'additionalPropertieS to 'false' in every node of the schema is a mandatory requirement.
B) When extracting specific entities from documents using SAI EXTRACT or '!PREDICT , it is often more effective to fine-tune a Document AI model for complex or varied document layouts rather than relying solely on extensive prompt engineering for zero-shot extraction.
C) For data enrichment involving classification with 'AI_CLASSIFY' , using descriptive and mutually exclusive categories in plain English, along with an optional clear task description, can significantly improve classification accuracy.
D) When performing sentiment analysis on customer feedback using 'AI_SENTIMENT, it's best practice to pass detailed, multi-turn conversation history to the function to enhance accuracy, similar to how 'AI_COMPLETE handles conversational context.
E) To manage costs effectively for LLM functions like SAI COMPLETE in a pipeline, always use the largest available warehouse size (e.g., 6XL Snowpark- optimized) to maximize throughput, as this directly reduces the overall token processing time and cost.

5. A security architect is configuring access controls for a new custom role, 'document_processor_role' , which will manage Document AI operations within a designated database 'doc_processing_db' and schema 'doc_workflow_schema'. The goal is to grant only the minimum essential database-level role required to begin working with Document AI features.

A)

B)

C)

D)

E)

Solutions: