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Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A Snowflake developer is tasked with enhancing a daily data pipeline. The pipeline processes raw text descriptions of system events and needs to extract structured information, specifically the (string) and its (string, restricted to 'low', 'medium', 'high', 'critical'). The output must be a strictly formatted JSON object, ensuring data quality for downstream analytics.
Consider the following SQL snippet intended for this transformation:

Which of the following statements are correct regarding this implementation and best practices for using with structured outputs in a data pipeline?

A) For all models supported by 'AI_COMPLETE' Structured Outputs, the 'additionalPropertieS field must be set to 'false' in every node of the schema, and the 'required' field must contain all property names.
B) Setting 'temperature 'to '0.7 ' is optimal for ensuring the most consistent and deterministic JSON outputs, especially for complex extraction tasks.
C) The complexity of the JSON schema, particularly deep nesting, does not impact the number of tokens processed and billed for 'AI_COMPLETE Structured Outputs.
D) Using 'TRY COMPLETE instead of would allow the pipeline to gracefully handle cases where the model fails to generate a valid JSON response by returning 'NULL' instead of an error.
E) The 'response_format' correctly defines the expected JSON structure, using 'enum' for 'severity_lever and 'required' to ensure 'event_name' and severity_lever are always present if extracted.

2. A Gen AI Specialist is setting up their Snowflake environment to deploy a high-performance open-source LLM for real-time inference using Snowpark Container Services (SPCS). They need to create a compute pool that can leverage NVIDIAAIOG GPUs to optimize model performance. Which of the following SQL statements correctly creates a compute pool capable of supporting an intensive GPU usage scenario, such as serving LLMs, while adhering to common configuration best practices for a new, small-scale deployment in Snowpark Container Services?

A)

B)

C)

D)

E)

3. A data team is deploying a new customer service chatbot using Snowflake Cortex Analyst. To accurately forecast and optimize their costs, the team needs to understand how Cortex Analyst billing works. Which of the following statements accurately describe the cost considerations for Snowflake Cortex Analyst?

A) Cortex Analyst's Cloud Services compute costs are subject to a daily adjustment, where Snowflake only bills if these costs exceed 10% of the daily virtual warehouse cost for the account.
B) Costs for Cortex Analyst are primarily driven by the number of successful messages processed, with a rate of 0.067 Credits per message.
C) Virtual warehouse compute is directly billed for Cortex Analyst operations, and its size can be adjusted to optimize query generation performance and cost.
D) Cortex Analyst incurs compute costs based on the total number of input and output tokens processed in each conversation turn.
E) Only successful responses (HTTP 200) from Cortex Analyst are counted towards the credit usage.

4. 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) Rely solely on a comprehensive list of 'stop sequences' to end generation when the summary is complete.
B) Implement 'first principles thinking' in the prompt, clearly outlining logical steps for summarization, and set 'temperature' to 0 for deterministic output.
C) 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.
D) Use a system prompt defining a persona like 'creative writer' and set 'temperature' to 0.9 to encourage diverse summaries.
E) Provide a 'task description' focusing on broad themes and set 'top_p' to 0.5 to balance creativity and relevance.

5. A development team is implementing a document retrieval system in Snowflake. They plan to store document embeddings and use VECTOR_L2_DISTANCE to find the most relevant documents for a given query embedding. Considering Snowflake's capabilities, which of the following statements are true regarding the use of vector types and VECTOR_L2_DISTANCE
? (Select all that apply)

A) O When defining a table column for 1024-dimensional float embeddings, the SQL type specification
B) VECTOR
C) To prevent issues with direct vector comparisons, explicitly using
D) Using the Snowpark Python library, developers can directly invoke

E) Document embeddings, which are typically float arrays, can be stored in a

Solutions: